@Article{info:doi/10.2196/62754,
author="Chevalier, Aline
and Dosso, Cheyenne",
title="The Influence of Medical Expertise and Information Search Skills on Medical Information Searching: Comparative Analysis From a Free Data Set",
journal="JMIR Form Res",
year="2025",
month="Apr",
day="17",
volume="9",
pages="e62754",
keywords="information searching",
keywords="credibility",
keywords="internet",
keywords="medicine",
keywords="information search skills",
abstract="Background: Nowadays, the internet has become the primary source of information for physicians seeking answers to medical questions about their patients before consulting colleagues. However, many websites provide low-quality, unreliable information that lacks scientific validation. Therefore, physicians must develop strong information search skills to locate relevant, accurate, and evidence-based content. However, previous studies have shown that physicians often have poor search skills and struggle to find information on the web, which may have detrimental consequences for patient care. Objective: This study aims to determine how medical students and residents searched for medical information on the internet, the quality of the web resources they used (including their nature and credibility), and how they evaluated the reliability of these resources and the answers they provided. Given the importance of domain knowledge (in this case, medicine) and information search skills in the search process, we compared the search behaviors of medical students and residents with those of computer science students. While medical students and residents possess greater medical-related knowledge, computer science students have stronger information search skills. Methods: A total of 20 students participated in this study: 10 medical students and residents, and 10 computer science students. Data were extracted from a freely accessible data set in accordance with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. All participants searched for medical information online to make a diagnosis, select a treatment, and enhance their knowledge of a medical condition---3 primary activities they commonly perform. We analyzed search performance metrics, including search time, the use of medical-related keywords, and the accuracy of the information found, as well as the nature and credibility of web resources used by medical students and residents compared with computer science students. Results: Medical students and residents provided more accurate answers than computer science students without requiring additional time. Their medical expertise also enabled them to better assess the reliability of resources and select high-quality web sources, primarily from hospital websites. However, it is noteworthy that they made limited use of evidence-based tools such as PubMed. Conclusions: Although medical students and residents generally outperformed computer science students, they did not frequently use evidence-based tools. As previously observed, they may avoid databases due to the risk of encountering too many irrelevant articles and difficulties in applying appropriate filters to locate relevant information. Nevertheless, clinical and practical evidence-based medicine plays a crucial role in updating physicians' knowledge, improving patient care, and enhancing physician-patient relationships. Therefore, information search skills should be an integral part of medical education and continuing professional development for physicians. ",
doi="10.2196/62754",
url="https://formative.jmir.org/2025/1/e62754"
}


@Article{info:doi/10.2196/67361,
author="Amoozegar, B. Jacqueline
and Williams, Peyton
and Giombi, C. Kristen
and Richardson, Courtney
and Shenkar, Ella
and Watkins, L. Rebecca
and O'Donoghue, C. Amie
and Sullivan, W. Helen",
title="Consumer Engagement With Risk Information on Prescription Drug Social Media Pages: Findings From In-Depth Interviews",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="25",
volume="27",
pages="e67361",
keywords="social media",
keywords="prescription drugs",
keywords="risk information",
keywords="safety information",
keywords="Facebook",
keywords="Instagram",
keywords="prescription",
keywords="risk",
keywords="information",
keywords="safety",
keywords="interview",
keywords="consumer engagement",
keywords="digital",
keywords="drug promotion",
keywords="user experience",
keywords="promotion",
abstract="Background: The volume of digital drug promotion has grown over time, and social media has become a source of information about prescription drugs for many consumers. Pharmaceutical companies currently present risk information about prescription drugs they promote in a variety of ways within and across social media platforms. There is scarce research on consumers' interactions with prescription drug promotion on social media, particularly on which features may facilitate or inhibit consumers' ability to find, review, and comprehend drug information. This is concerning because it is critical for consumers to know and weigh drug benefits and risks to be able to make informed decisions regarding medical treatment. Objective: We aimed to develop an understanding of the user interface (UI) and user experience (UX) of social media pages and posts created by pharmaceutical companies to promote drugs and how UI or UX design features impact consumers' interactions with drug information. Methods: We conducted in-person interviews with 54 consumers segmented into groups by device type (laptop or mobile phone), social media platform (Facebook or Instagram), and age. Interviewers asked participants to navigate to and review a series of 4 pages and 3 posts on their assigned device and platform. Interviewers encouraged participants to ``think aloud,'' as they interacted with the stimuli during a brief observation period. Following each observation period, participants were asked probing questions. An analyst reviewed video recordings of the observation periods to abstract quantitative interaction data on whether a participant clicked on or viewed risk information at each location it appeared on each page. Participants' responses were organized in a metamatrix, which we used to conduct thematic analysis. Results: Observational data revealed that 59\% of participants using Facebook and 70\% of participants using Instagram viewed risk information in at least 1 possible location on average across all pages tested during the observation period. There was not a single location across the Facebook pages that participants commonly clicked on to view risk information. However, a video with scrolling risk information attracted more views than other features. On Instagram, at least half of the participants consistently clicked on the highlighted story with risk information across the pages. Although thematic analysis showed that most participants were able to identify the official pages and risk information for each drug, auto-scrolling text and text size posed barriers to identification and comprehensive review for some participants. Participants generally found it more difficult to identify the drugs' indications than risks. Participants using Instagram more frequently reported challenges identifying risks and indications compared to those using Facebook. Conclusions: UI or UX design features can facilitate or pose barriers to users' identification, review, and comprehension of the risk information provided on prescription drugs' social media pages and posts. ",
doi="10.2196/67361",
url="https://www.jmir.org/2025/1/e67361"
}


@Article{info:doi/10.2196/67658,
author="Tistad, Malin
and Hultman, Lill
and Wohlin Wottrich, Annica
and von Koch, Lena",
title="The Lived Experience of Participating in Online Peer-To-Peer Groups After Acquired Brain Injury: Phenomenological Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="25",
volume="27",
pages="e67658",
keywords="compassion",
keywords="experiential knowledge",
keywords="fatigue",
keywords="self-compassion",
keywords="stroke",
keywords="social media",
keywords="meaning",
keywords="interview",
keywords="normalization",
abstract="Background: Stroke and other acquired brain injuries (ABIs) can present challenging experiences for individuals, both in recovery of functions affected by visible or invisible impairments and in learning to live with the new situation. Research has shown that sharing experiences face-to-face in peer groups can be beneficial during recovery. However, there is limited knowledge about the lived experiences of people with ABI who participate in online peer-to-peer groups. Objective: The aim of our study was to explore the lived experiences of participating in online peer-to-peer groups for people with ABI, where participants themselves set the agenda. Methods: Members of 2 Facebook groups (FBGs) for people with ABI were invited to participate in this study, and 20 individuals were included (14 women and 6 men; age range 24-74 years). One FBG focused on stroke and the other on fatigue caused by ABI. One group was private, and the other group was public. Data were collected through semistructured interviews, in which participants were encouraged to describe their experiences of engaging in FBGs in detail. The interviews were conducted over telephone or Zoom and digitally recorded. The audio recordings were then transcribed verbatim, resulting in 224 pages of text, and analyzed using the empirical phenomenological psychological method. Results: The analysis presented a common meaning structure with 1 main characteristic that is, ``validating self,'' common for all 20 participants, and 3 subcharacteristics, that is, ``learning---having one's own experiences confirmed,'' ``adjusting self---building competence and self-compassion,'' and ``supporting others---becoming a valued lived-experience expert/authority.'' Together, the subcharacteristics reflected a process of validating self from newcomer to lived-experience expert or authority. In this process, members of FBGs moved from being newcomers with pronounced needs for support and to learn and to have their experiences confirmed by others with similar experiences. Thus, participants were building competence and developing self-compassion. Gradually, they assumed the role of advisors, mentors, or coaches, acknowledging their experiences and competence as valuable to others, thereby validating themselves as compassionate lived-experience experts or authorities in supporting others. Conclusions: Participation in online peer-to-peer groups can offer unique opportunities for individuals with ABI to validate self through processes that involve learning, developing self-compassion and compassion for others, and offering support to others with similar experiences. Given that rehabilitation after an ABI is often of limited duration and that positive experiences can be achieved over time through involvement in digital peer-to-peer support, health care professionals should assist patients by providing information and directing them to digital networks for people with ABI. However, when recommending the use of online peer-to-peer support, impairments and insufficient digital competence that may complicate or prevent the use of social media should be assessed and support provided when relevant. ",
doi="10.2196/67658",
url="https://www.jmir.org/2025/1/e67658",
url="http://www.ncbi.nlm.nih.gov/pubmed/40131323"
}


@Article{info:doi/10.2196/67677,
author="Hou, Yu
and Bishop, R. Jeffrey
and Liu, Hongfang
and Zhang, Rui",
title="Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="19",
volume="27",
pages="e67677",
keywords="dietary supplements",
keywords="knowledge representation",
keywords="knowledge graph",
keywords="retrieval-augmented generation",
keywords="large language model",
keywords="user interface",
abstract="Background: Dietary supplements (DSs) are widely used to improve health and nutrition, but challenges related to misinformation, safety, and efficacy persist due to less stringent regulations compared with pharmaceuticals. Accurate and reliable DS information is critical for both consumers and health care providers to make informed decisions. Objective: This study aimed to enhance DS-related question answering by integrating an advanced retrieval-augmented generation (RAG) system with the integrated Dietary Supplement Knowledgebase 2.0 (iDISK2.0), a dietary supplement knowledge base, to improve accuracy and reliability. Methods: We developed iDISK2.0 by integrating updated data from authoritative sources, including the Natural Medicines Comprehensive Database, the Memorial Sloan Kettering Cancer Center database, Dietary Supplement Label Database, and Licensed Natural Health Products Database, and applied advanced data cleaning and standardization techniques to reduce noise. The RAG system combined the retrieval power of a biomedical knowledge graph with the generative capabilities of large language models (LLMs) to address limitations of stand-alone LLMs, such as hallucination. The system retrieves contextually relevant subgraphs from iDISK2.0 based on user queries, enabling accurate and evidence-based responses through a user-friendly interface. We evaluated the system using true-or-false and multiple-choice questions derived from the Memorial Sloan Kettering Cancer Center database and compared its performance with stand-alone LLMs. Results: iDISK2.0 integrates 174,317 entities across 7 categories, including 8091 dietary supplement ingredients; 163,806 dietary supplement products; 786 diseases; and 625 drugs, along with 6 types of relationships. The RAG system achieved an accuracy of 99\% (990/1000) for true-or-false questions on DS effectiveness and 95\% (948/100) for multiple-choice questions on DS-drug interactions, substantially outperforming stand-alone LLMs like GPT-4o (OpenAI), which scored 62\% (618/1000) and 52\% (517/1000) on these respective tasks. The user interface enabled efficient interaction, supporting free-form text input and providing accurate responses. Integration strategies minimized data noise, ensuring access to up-to-date, DS-related information. Conclusions: By integrating a robust knowledge graph with RAG and LLM technologies, iDISK2.0 addresses the critical limitations of stand-alone LLMs in DS information retrieval. This study highlights the importance of combining structured data with advanced artificial intelligence methods to improve accuracy and reduce misinformation in health care applications. Future work includes extending the framework to broader biomedical domains and improving evaluation with real-world, open-ended queries. ",
doi="10.2196/67677",
url="https://www.jmir.org/2025/1/e67677"
}


@Article{info:doi/10.2196/66279,
author="{\vS}uvalov, Hendrik
and Lepson, Mihkel
and Kukk, Veronika
and Malk, Maria
and Ilves, Neeme
and Kuulmets, Hele-Andra
and Kolde, Raivo",
title="Using Synthetic Health Care Data to Leverage Large Language Models for Named Entity Recognition: Development and Validation Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="18",
volume="27",
pages="e66279",
keywords="natural language processing",
keywords="named entity recognition",
keywords="large language model",
keywords="synthetic data",
keywords="LLM",
keywords="NLP",
keywords="machine learning",
keywords="artificial intelligence",
keywords="language model",
keywords="NER",
keywords="medical entity",
keywords="Estonian",
keywords="health care data",
keywords="annotated data",
keywords="data annotation",
keywords="clinical decision support",
keywords="data mining",
abstract="Background: Named entity recognition (NER) plays a vital role in extracting critical medical entities from health care records, facilitating applications such as clinical decision support and data mining. Developing robust NER models for low-resource languages, such as Estonian, remains a challenge due to the scarcity of annotated data and domain-specific pretrained models. Large language models (LLMs) have proven to be promising in understanding text from any language or domain. Objective: This study addresses the development of medical NER models for low-resource languages, specifically Estonian. We propose a novel approach by generating synthetic health care data and using LLMs to annotate them. These synthetic data are then used to train a high-performing NER model, which is applied to real-world medical texts, preserving patient data privacy. Methods: Our approach to overcoming the shortage of annotated Estonian health care texts involves a three-step pipeline: (1) synthetic health care data are generated using a locally trained GPT-2 model on Estonian medical records, (2) the synthetic data are annotated with LLMs, specifically GPT-3.5-Turbo and GPT-4, and (3) the annotated synthetic data are then used to fine-tune an NER model, which is later tested on real-world medical data. This paper compares the performance of different prompts; assesses the impact of GPT-3.5-Turbo, GPT-4, and a local LLM; and explores the relationship between the amount of annotated synthetic data and model performance. Results: The proposed methodology demonstrates significant potential in extracting named entities from real-world medical texts. Our top-performing setup achieved an F1-score of 0.69 for drug extraction and 0.38 for procedure extraction. These results indicate a strong performance in recognizing certain entity types while highlighting the complexity of extracting procedures. Conclusions: This paper demonstrates a successful approach to leveraging LLMs for training NER models using synthetic data, effectively preserving patient privacy. By avoiding reliance on human-annotated data, our method shows promise in developing models for low-resource languages, such as Estonian. Future work will focus on refining the synthetic data generation and expanding the method's applicability to other domains and languages. ",
doi="10.2196/66279",
url="https://www.jmir.org/2025/1/e66279"
}


@Article{info:doi/10.2196/67033,
author="Hao, Jie
and Chen, Zhenli
and Peng, Qinglong
and Zhao, Liang
and Zhao, Wanqing
and Cong, Shan
and Li, Junlian
and Li, Jiao
and Qian, Qing
and Sun, Haixia",
title="Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="18",
volume="27",
pages="e67033",
keywords="prompt engineering",
keywords="named entity recognition",
keywords="in-context learning",
keywords="large language model",
keywords="Chinese medical literature",
keywords="measurement-based care",
keywords="framework",
keywords="prompt",
keywords="prompt framework",
keywords="scale",
keywords="China",
keywords="medical literature",
keywords="MBC",
keywords="LLM",
keywords="MedScaleNER",
keywords="retrieval",
keywords="information retrieval",
keywords="dataset",
keywords="artificial intelligence",
keywords="AI",
abstract="Background: Measurement-based care improves patient outcomes by using standardized scales, but its widespread adoption is hindered by the lack of accessible and structured knowledge, particularly in unstructured Chinese medical literature. Extracting scale-related knowledge entities from these texts is challenging due to limited annotated data. While large language models (LLMs) show promise in named entity recognition (NER), specialized prompting strategies are needed to accurately recognize medical scale-related entities, especially in low-resource settings. Objective: This study aims to develop and evaluate MedScaleNER, a task-oriented prompt framework designed to optimize LLM performance in recognizing medical scale-related entities from Chinese medical literature. Methods: MedScaleNER incorporates demonstration retrieval within in-context learning, chain-of-thought prompting, and self-verification strategies to improve performance. The framework dynamically retrieves optimal examples using a k-nearest neighbors approach and decomposes the NER task into two subtasks: entity type identification and entity labeling. Self-verification ensures the reliability of the final output. A dataset of manually annotated Chinese medical journal papers was constructed, focusing on three key entity types: scale names, measurement concepts, and measurement items. Experiments were conducted by varying the number of examples and the proportion of training data to evaluate performance in low-resource settings. Additionally, MedScaleNER's performance was compared with locally fine-tuned models. Results: The CMedS-NER (Chinese Medical Scale Corpus for Named Entity Recognition) dataset, containing 720 papers with 27,499 manually annotated scale-related knowledge entities, was used for evaluation. Initial experiments identified GLM-4-0520 as the best-performing LLM among six tested models. When applied with GLM-4-0520, MedScaleNER significantly improved NER performance for scale-related entities, achieving a macro F1-score of 59.64\% in an exact string match with the full training dataset. The highest performance was achieved with 20-shot demonstrations. Under low-resource scenarios (eg, 1\% of the training data), MedScaleNER outperformed all tested locally fine-tuned models. Ablation studies highlighted the importance of demonstration retrieval and self-verification in improving model reliability. Error analysis revealed four main types of mistakes: identification errors, type errors, boundary errors, and missing entities, indicating areas for further improvement. Conclusions: MedScaleNER advances the application of LLMs and prompts engineering for specialized NER tasks in Chinese medical literature. By addressing the challenges of unstructured texts and limited annotated data, MedScaleNER's adaptability to various biomedical contexts supports more efficient and reliable knowledge extraction, contributing to broader measurement-based care implementation and improved clinical and research outcomes. ",
doi="10.2196/67033",
url="https://www.jmir.org/2025/1/e67033"
}


@Article{info:doi/10.2196/66683,
author="Li, Hongmin
and Li, Dongxu
and Zhai, Min
and Lin, Li
and Cao, ZhiHeng",
title="Associations Among Online Health Information Seeking Behavior, Online Health Information Perception, and Health Service Utilization: Cross-Sectional Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="14",
volume="27",
pages="e66683",
keywords="online health information seeking (OHIS)",
keywords="online health information perception (OHIP)",
keywords="mediating effect",
keywords="health service utilization",
keywords="health information",
keywords="health perception",
keywords="data",
keywords="China",
keywords="Chinese General Social Survey (CGSS)",
keywords="database",
keywords="medical information",
keywords="survey",
abstract="Background: Seeking online health information can empower individuals to better understand their health concerns, facilitating their ability to manage their health conditions more effectively. It has the potential to change the likelihood and frequency of health service usage. Although existing literature has demonstrated the prevalence of seeking online health information among different populations, the factors affecting online health information perception and discussions on the associations between seeking online health information and health service utilization are limited. Objective: We analyzed the associations between online health information seeking behavior and health service utilization, as well as the online health information perception delivery mechanism. Methods: We analyzed data from the Chinese General Social Survey, the first national representative survey conducted in mainland China. The independent variable was the online health information seeking behavior. The outcome variable was health service utilization by the respondents, and online health information perception was selected as the mediating variable in this analysis. Factor analysis was conducted to obtain online health information perception. Multiple regressions were performed to investigate the effect of online health information seeking behavior on physician visits. Bootstrap methods were conducted to test the mediation effects of online health information perception. Results: This analysis included 1475 cases. Among the participants, 939 (63.66\%) sought online health information in the last 12 months. The mean age of the respondents was 46.72 (SD 15.86) years, and 794 (53.83\%) were females. After controlling for other variables, individuals with online health information seeking behaviors showed 0.289 times more outpatient visits (P=.003), 0.131 times more traditional Chinese medicine outpatient visits (P=.01), and 0.158 times more Western medicine outpatient visits (P=.007) over the past year compared to those who did not seek health information online. Additionally, multiple regression analyses revealed statistically significant effects of gender, age, and health status on physician visits. The total effect revealed that seeking online health information significantly influenced the total physician visits ($\beta$=0.290; P=.003), indicating a certain correlation between online health information seeking behavior and physician visits. Seeking online health information had a significant positive impact on the perception ($\beta$=0.265; P<.001). The mediation effects analysis identified that online health information perception led to a significant increase in physician visits with the increase in the online health information seeking behaviors ($\beta$=0.232; P=.02). Conclusions: The online health information perception of an individual influences the effect online health information seeking has on the frequency of physician visits. The online health information seeking behavior impacts outpatient service utilization both directly and indirectly through online health information perception and significantly increases the frequency of clinic visits after controlling for other variables. Interventions can be explored to improve the health utilization of residents by increasing their online health information perception. ",
doi="10.2196/66683",
url="https://www.jmir.org/2025/1/e66683"
}


@Article{info:doi/10.2196/51804,
author="Portela, Diana
and Freitas, Alberto
and Costa, El{\'i}sio
and Giovannini, Mattia
and Bousquet, Jean
and Almeida Fonseca, Jo{\~a}o
and Sousa-Pinto, Bernardo",
title="Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="10",
volume="27",
pages="e51804",
keywords="infodemiology",
keywords="asthma",
keywords="administrative databases",
keywords="multimorbidity",
keywords="co-morbidity",
keywords="respiratory",
keywords="pulmonary",
keywords="Google Trends",
keywords="correlation",
keywords="hospitalization",
keywords="admissions",
keywords="autoregressive",
keywords="information seeking",
keywords="searching",
keywords="searches",
keywords="forecasting",
abstract="Background: Google Trends (GT) data have shown promising results as a complementary tool to classical surveillance approaches. However, GT data are not necessarily provided by a representative sample of patients and may be skewed toward demographic and clinical groups that are more likely to use the internet to search for their health. Objective: In this study, we aimed to assess whether GT-based models perform differently in distinct population subgroups. To assess that, we analyzed a case study on asthma hospitalizations. Methods: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 3 different countries (Portugal, Spain, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold for the same countries and time period were retrieved from GT. We estimated the correlation between GT data and the weekly occurrence of asthma hospitalizations (considering separate asthma admissions data according to patients' age, sex, ethnicity, and presence of comorbidities). In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations (for the different aforementioned subgroups) for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results: Overall, correlation coefficients between GT on the pseudo-influenza syndrome topic and asthma hospitalizations ranged between 0.33 (in Portugal for admissions with at least one Charlson comorbidity group) and 0.86 (for admissions in women and in White people in Brazil). In the 3 assessed countries, forecasted hospitalizations for 2015-2016 correlated more strongly with observed admissions of older versus younger individuals (Portugal: Spearman $\rho$=0.70 vs $\rho$=0.56; Spain: $\rho$=0.88 vs $\rho$=0.76; Brazil: $\rho$=0.83 vs $\rho$=0.82). In Portugal and Spain, forecasted hospitalizations had a stronger correlation with admissions occurring for women than men (Portugal: $\rho$=0.75 vs $\rho$=0.52; Spain: $\rho$=0.83 vs $\rho$=0.51). In Brazil, stronger correlations were observed for admissions of White than of Black or Brown individuals ($\rho$=0.92 vs $\rho$=0.87). In Portugal, stronger correlations were observed for admissions of individuals without any comorbidity compared with admissions of individuals with comorbidities ($\rho$=0.68 vs $\rho$=0.66). Conclusions: We observed that the models based on GT data may perform differently in demographic and clinical subgroups of participants, possibly reflecting differences in the composition of internet users' health-seeking behaviors. ",
doi="10.2196/51804",
url="https://www.jmir.org/2025/1/e51804"
}


@Article{info:doi/10.2196/57881,
author="Muscat, M. Danielle
and Hinton, Rachael
and Kuruvilla, Shyama
and Nutbeam, Don",
title="``Your Life, Your Health: Tips and Information for Health and Well-Being'': Development of a World Health Organization Digital Resource to Support Universal Access to Trustworthy Health Information",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="6",
volume="9",
pages="e57881",
keywords="health communication",
keywords="health literacy",
keywords="consumer health information",
keywords="digital health",
keywords="universal health care",
abstract="Background: Access to trustworthy, understandable, and actionable health information is a key determinant of health and is an essential component of universal health coverage and primary health care. The World Health Organization has developed a new digital resource for the general public to improve health and well-being across different life phases and to support people in caring for themselves, their families, and their communities. The goal was to make trustworthy health information accessible, understandable, and actionable for the general public in a digital format and at the global scale. Objective: The aim of this paper was to describe the multistage approach and methodology used to develop the resource Your life, your health: Tips and information for health and well-being (hereafter, Your life, your health). Methods: A 5-step process was used to develop Your life, your health, including (1) reviewing and synthesizing existing World Health Organization technical guidance, member state health and health literacy plans, and international human rights frameworks to identify priority messages; (2) developing messages and graphics that are accessible, understandable, and actionable for the public using health literacy principles; (3) engaging with experts and stakeholders to refine messages and message delivery; (4) presenting priority content in an accessible digital format; and (5) adapting the resource based on feedback and new evidences. Results: The Your life, your health online resource adopts a life-course approach to organize health information based on priority actions and rights that support peoples' health and well-being across different life stages and specific health topics. The resource promotes health literacy by offering advice on asking questions to health workers, making informed decisions about personal and family health, and effectively using digital media to obtain reliable health information. Additionally, it reflects the ambitions of the Sustainable Development Goals by providing essential information on the social determinants of health and clarifies the distinct roles of individuals, frontline workers, governments, and the media in promoting and protecting health. Conclusions: Making health information available---including to the public---is an essential step in strengthening the global health information system. The development process for the Your life, your health online resource outlined in this article offers a structured approach to translate technical health guidelines into accessible, understandable, and actionable health information for the general public. ",
doi="10.2196/57881",
url="https://formative.jmir.org/2025/1/e57881"
}


@Article{info:doi/10.2196/64672,
author="DuPont-Reyes, J. Melissa
and Villatoro, P. Alice
and Tang, Lu",
title="Health Information Scanning and Seeking in Diverse Language, Cultural and Technological Media Among Latinx Adolescents: Cross-Sectional Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="5",
volume="27",
pages="e64672",
keywords="adolescent behaviors",
keywords="mental health",
keywords="Latino",
keywords="social media",
keywords="adolescent",
keywords="media use",
keywords="internet use",
keywords="health information seeking",
keywords="health information scanning",
keywords="mobile phone",
abstract="Background: Continuous scientific and policy debate regarding the potential harm and/or benefit of media and social media on adolescent health has resulted, in part, from a deficiency in robust scientific evidence. Even with a lack of scientific consensus, public attitudes, and sweeping social media prohibitions have swiftly ensued. A focus on the diversity of adolescents around the world and their diverse use of language, culture, and social media is absent from these discussions. Objective: This study aims to guide communication policy and practice, including those addressing access to social media by adolescent populations. This study assesses physical and mental health information scanning and seeking behaviors across diverse language, cultural, and technological media and social media among Latinx adolescent residents in the United States. This study also explores how Latinx adolescents with mental health concerns use media and social media for support. Methods: In 2021, a cross-sectional survey was conducted among 701 US-based Latinx adolescents aged 13-20 years to assess their health-related media use. Assessments ascertained the frequency of media use and mental and physical health information scanning and seeking across various media technologies (eg, TV, podcasts, and social media) and language and cultural types (ie, Spanish, Latinx-tailored English, and general English). Linear regression models were used to estimate adjusted predicted means of mental and physical health information scanning and seeking across diverse language and cultural media types, net personal and family factors, in the full sample and by subsamples of mental health symptoms (moderate-high vs none-mild). Results: Among Latinx adolescents, media and social media use was similar across mental health symptoms. However, Latinx adolescents with moderate-high versus none-mild symptoms more often scanned general English media and social media for mental health information (P<.05), although not for physical health information. Also, Latinx adolescents with moderate-high versus none-mild symptoms more often sought mental health information on Latinx-tailored and general English media, and social media (P<.05); a similar pattern was found for physical health information seeking. In addition, Latinx adolescents with moderate-high versus none-mild symptoms often sought help from family and friends for mental and physical health problems and health care providers for mental health only (P<.05). Conclusions: While media and social media usage was similar across mental health, Latinx adolescents with moderate-high symptoms more often encountered mental health content in general English media and social media and turned to general English- and Latinx-tailored media and social media more often for their health concerns. Together these study findings suggest more prevalent and available mental health content in general English versus Spanish language and Latinx-tailored media and underscore the importance of providing accessible, quality health information across diverse language, cultural, and technological media and social networks as a viable opportunity to help improve adolescent health. ",
doi="10.2196/64672",
url="https://www.jmir.org/2025/1/e64672",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053766"
}


@Article{info:doi/10.2196/56831,
author="Bayani, Azadeh
and Ayotte, Alexandre
and Nikiema, Noel Jean",
title="Transformer-Based Tool for Automated Fact-Checking of Online Health Information: Development Study",
journal="JMIR Infodemiology",
year="2025",
month="Feb",
day="21",
volume="5",
pages="e56831",
keywords="fact-checking automation",
keywords="transformers",
keywords="infodemic",
keywords="credible health information",
keywords="machine learning",
keywords="automated",
keywords="online health information",
keywords="misinformation",
keywords="natural language processing",
keywords="epidemiology",
keywords="health domain",
abstract="Background: Many people seek health-related information online. The significance of reliable information became particularly evident due to the potential dangers of misinformation. Therefore, discerning true and reliable information from false information has become increasingly challenging. Objective: This study aimed to present a pilot study in which we introduced a novel approach to automate the fact-checking process, leveraging PubMed resources as a source of truth using natural language processing transformer models to enhance the process. Methods: A total of 538 health-related web pages, covering 7 different disease subjects, were manually selected by Factually Health Company. The process included the following steps: (1) using transformer models of bidirectional encoder representations from transformers (BERT), BioBERT, and SciBERT, and traditional models of random forests and support vector machines, to classify the contents of web pages into 3 thematic categories (semiology, epidemiology, and management), (2) for each category in the web pages, a PubMed query was automatically produced using a combination of the ``WellcomeBertMesh'' and ``KeyBERT'' models, (3) top 20 related literatures were automatically extracted from PubMed, and finally, (4) the similarity checking techniques of cosine similarity and Jaccard distance were applied to compare the content of extracted literature and web pages. Results: The BERT model for the categorization of web page contents had good performance, with F1-scores and recall of 93\% and 94\% for semiology and epidemiology, respectively, and 96\% for both the recall and F1-score for management. For each of the 3 categories in a web page, 1 PubMed query was generated and with each query, the 20 most related, open access articles within the category of systematic reviews and meta-analyses were extracted. Less than 10\% of the extracted literature was irrelevant; those were deleted. For each web page, an average of 23\% of the sentences were found to be very similar to the literature. Moreover, during the evaluation, it was found that cosine similarity outperformed the Jaccard distance measure when comparing the similarity between sentences from web pages and academic papers vectorized by BERT. However, there was a significant issue with false positives in the retrieved sentences when compared with accurate similarities, as some sentences had a similarity score exceeding 80\%, but they could not be considered similar sentences. Conclusions: In this pilot study, we have proposed an approach to automate the fact-checking of health-related online information. Incorporating content from PubMed or other scientific article databases as trustworthy resources can automate the discovery of similarly credible information in the health domain. ",
doi="10.2196/56831",
url="https://infodemiology.jmir.org/2025/1/e56831",
url="http://www.ncbi.nlm.nih.gov/pubmed/39812653"
}


@Article{info:doi/10.2196/66910,
author="Seinen, M. Tom
and Kors, A. Jan
and van Mulligen, M. Erik
and Rijnbeek, R. Peter",
title="Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="13",
volume="27",
pages="e66910",
keywords="natural language processing",
keywords="named entity recognition",
keywords="clinical concept extraction",
keywords="machine learning",
keywords="electronic health records",
keywords="EHR",
keywords="word embeddings",
keywords="clinical concept similarity",
keywords="text mining",
keywords="code",
keywords="free-text",
keywords="information",
keywords="electronic record",
keywords="data",
keywords="patient records",
keywords="framework",
keywords="structured data",
keywords="unstructured data",
abstract="Background: Electronic health records (EHRs) consist of both structured data (eg, diagnostic codes) and unstructured data (eg, clinical notes). It is commonly believed that unstructured clinical narratives provide more comprehensive information. However, this assumption lacks large-scale validation and direct validation methods. Objective: This study aims to quantitatively compare the information in structured and unstructured EHR data and directly validate whether unstructured data offers more extensive information across a patient population. Methods: We analyzed both structured and unstructured data from patient records and visits in a large Dutch primary care EHR database between January 2021 and January 2024. Clinical concepts were identified from free-text notes using an extraction framework tailored for Dutch and compared with concepts from structured data. Concept embeddings were generated to measure semantic similarity between structured and extracted concepts through cosine similarity. A similarity threshold was systematically determined via annotated matches and minimized weighted Gini impurity. We then quantified the concept overlap between structured and unstructured data across various concept domains and patient populations. Results: In a population of 1.8 million patients, only 13\% of extracted concepts from patient records and 7\% from individual visits had similar structured counterparts. Conversely, 42\% of structured concepts in records and 25\% in visits had similar matches in unstructured data. Condition concepts had the highest overlap, followed by measurements and drug concepts. Subpopulation visits, such as those with chronic conditions or psychological disorders, showed different proportions of data overlap, indicating varied reliance on structured versus unstructured data across clinical contexts. Conclusions: Our study demonstrates the feasibility of quantifying the information difference between structured and unstructured data, showing that the unstructured data provides important additional information in the studied database and populations. The annotated concept matches are made publicly available for the clinical natural language processing community. Despite some limitations, our proposed methodology proves versatile, and its application can lead to more robust and insightful observational clinical research. ",
doi="10.2196/66910",
url="https://www.jmir.org/2025/1/e66910"
}


@Article{info:doi/10.2196/66696,
author="Chen, Sihui
and Ngai, Bik Cindy Sing
and Cheng, Cecilia
and Hu, Yangna",
title="Analyzing Themes, Sentiments, and Coping Strategies Regarding Online News Coverage of Depression in Hong Kong: Mixed Methods Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="13",
volume="27",
pages="e66696",
keywords="online news coverage",
keywords="depression",
keywords="natural language processing",
keywords="NLP",
keywords="latent Dirichlet allocation",
keywords="LDA",
keywords="sentiment",
keywords="coping strategies",
keywords="content analysis",
abstract="Background: Depression, a highly prevalent global mental disorder, has prompted significant research concerning its association with social media use and its impact during Hong Kong's social unrest and COVID-19 pandemic. However, other mainstream media, specifically online news, has been largely overlooked. Despite extensive research conducted in countries, such as the United States, Australia, and Canada, to investigate the latent subthemes, sentiments, and coping strategies portrayed in depression-related news, the landscape in Hong Kong remains unexplored. Objective: This study aims to uncover the latent subthemes presented in the online news coverage of depression in Hong Kong, examine the sentiment conveyed in the news, and assess whether coping strategies have been provided in the news for individuals experiencing depression. Methods: This study used natural language processing (NLP) techniques, namely the latent Dirichlet allocation topic modeling and the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis, to fulfill the first and second objectives. Coping strategies were rigorously assessed and manually labeled with designated categories by content analysis. The online news was collected from February 2019 to May 2024 from Hong Kong mainstream news websites to examine the latest portrayal of depression, particularly during and after the social unrest and the COVID-19 pandemic. Results: In total, 2435 news articles were retained for data analysis after the news screening process. A total of 7 subthemes were identified based on the topic modeling results. Societal system, law enforcement, global recession, lifestyle, leisure, health issues, and US politics were the latent subthemes. Moreover, the overall news exhibited a slightly positive sentiment. The correlations between the sentiment scores and the latent subthemes indicated that the societal system, law enforcement, health issues, and US politics revealed negative tendencies, while the remainder leaned toward a positive sentiment. The coping strategies for depression were substantially lacking; however, the categories emphasizing information on skills and resources and individual adjustment to cope with depression emerged as the priority focus. Conclusions: This pioneering study used a mixed methods approach where NLP was used to investigate latent subthemes and underlying sentiment in online news. Content analysis was also performed to examine available coping strategies. The findings of this research enhance our understanding of how depression is portrayed through online news in Hong Kong and the preferable coping strategies being used to mitigate depression. The potential impact on readers was discussed. Future research is encouraged to address the mentioned implications and limitations, with recommendations to apply advanced NLP techniques to a new mental health issue case or language. ",
doi="10.2196/66696",
url="https://www.jmir.org/2025/1/e66696",
url="http://www.ncbi.nlm.nih.gov/pubmed/39946170"
}


@Article{info:doi/10.2196/64290,
author="Mendel, Tamir
and Singh, Nina
and Mann, M. Devin
and Wiesenfeld, Batia
and Nov, Oded",
title="Laypeople's Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries: Survey Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="13",
volume="27",
pages="e64290",
keywords="large language model",
keywords="artificial intelligence",
keywords="LLMs",
keywords="search engine",
keywords="Google",
keywords="internet",
keywords="online health information",
keywords="United States",
keywords="survey",
keywords="mobile phone",
abstract="Background: Laypeople have easy access to health information through large language models (LLMs), such as ChatGPT, and search engines, such as Google. Search engines transformed health information access, and LLMs offer a new avenue for answering laypeople's questions. Objective: We aimed to compare the frequency of use and attitudes toward LLMs and search engines as well as their comparative relevance, usefulness, ease of use, and trustworthiness in responding to health queries. Methods: We conducted a screening survey to compare the demographics of LLM users and nonusers seeking health information, analyzing results with logistic regression. LLM users from the screening survey were invited to a follow-up survey to report the types of health information they sought. We compared the frequency of use of LLMs and search engines using ANOVA and Tukey post hoc tests. Lastly, paired-sample Wilcoxon tests compared LLMs and search engines on perceived usefulness, ease of use, trustworthiness, feelings, bias, and anthropomorphism. Results: In total, 2002 US participants recruited on Prolific participated in the screening survey about the use of LLMs and search engines. Of them, 52\% (n=1045) of the participants were female, with a mean age of 39 (SD 13) years. Participants were 9.7\% (n=194) Asian, 12.1\% (n=242) Black, 73.3\% (n=1467) White, 1.1\% (n=22) Hispanic, and 3.8\% (n=77) were of other races and ethnicities. Further, 1913 (95.6\%) used search engines to look up health queries versus 642 (32.6\%) for LLMs. Men had higher odds (odds ratio [OR] 1.63, 95\% CI 1.34-1.99; P<.001) of using LLMs for health questions than women. Black (OR 1.90, 95\% CI 1.42-2.54; P<.001) and Asian (OR 1.66, 95\% CI 1.19-2.30; P<.01) individuals had higher odds than White individuals. Those with excellent perceived health (OR 1.46, 95\% CI 1.1-1.93; P=.01) were more likely to use LLMs than those with good health. Higher technical proficiency increased the likelihood of LLM use (OR 1.26, 95\% CI 1.14-1.39; P<.001). In a follow-up survey of 281 LLM users for health, most participants used search engines first (n=174, 62\%) to answer health questions, but the second most common first source consulted was LLMs (n=39, 14\%). LLMs were perceived as less useful (P<.01) and less relevant (P=.07), but elicited fewer negative feelings (P<.001), appeared more human (LLM: n=160, vs search: n=32), and were seen as less biased (P<.001). Trust (P=.56) and ease of use (P=.27) showed no differences. Conclusions: Search engines are the primary source of health information; yet, positive perceptions of LLMs suggest growing use. Future work could explore whether LLM trust and usefulness are enhanced by supplementing answers with external references and limiting persuasive language to curb overreliance. Collaboration with health organizations can help improve the quality of LLMs' health output. ",
doi="10.2196/64290",
url="https://www.jmir.org/2025/1/e64290",
url="http://www.ncbi.nlm.nih.gov/pubmed/39946180"
}


@Article{info:doi/10.2196/63149,
author="Downing, J. Gregory
and Tramontozzi, M. Lucas
and Garcia, Jackson
and Villanueva, Emma",
title="Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review",
journal="JMIR Ment Health",
year="2025",
month="Feb",
day="11",
volume="12",
pages="e63149",
keywords="health",
keywords="informatics",
keywords="internet search data",
keywords="early diagnosis",
keywords="web search",
keywords="information technology",
keywords="internet",
keywords="machine learning",
keywords="medical records",
keywords="diagnosis",
keywords="health care",
keywords="self-diagnosis",
keywords="detection",
keywords="intervention",
keywords="patient education",
keywords="internet search",
keywords="health-seeking behavior",
keywords="artificial intelligence",
keywords="AI",
abstract="Background: The integration of information technology into health care has created opportunities to address diagnostic challenges. Internet searches, representing a vast source of health-related data, hold promise for improving early disease detection. Studies suggest that patterns in search behavior can reveal symptoms before clinical diagnosis, offering potential for innovative diagnostic tools. Leveraging advancements in machine learning, researchers have explored linking search data with health records to enhance screening and outcomes. However, challenges like privacy, bias, and scalability remain critical to its widespread adoption. Objective: We aimed to explore the potential and challenges of using internet search data in medical diagnosis, with a specific focus on diseases and conditions such as cancer, cardiovascular disease, mental and behavioral health, neurodegenerative disorders, and nutritional and metabolic diseases. We examined ethical, technical, and policy considerations while assessing the current state of research, identifying gaps and limitations, and proposing future research directions to advance this emerging field. Methods: We conducted a comprehensive analysis of peer-reviewed literature and informational interviews with subject matter experts to examine the landscape of internet search data use in medical research. We searched for published peer-reviewed literature on the PubMed database between October and December 2023. Results: Systematic selection based on predefined criteria included 40 articles from the 2499 identified articles. The analysis revealed a nascent domain of internet search data research in medical diagnosis, marked by advancements in analytics and data integration. Despite challenges such as bias, privacy, and infrastructure limitations, emerging initiatives could reshape data collection and privacy safeguards. Conclusions: We identified signals correlating with diagnostic considerations in certain diseases and conditions, indicating the potential for such data to enhance clinical diagnostic capabilities. However, leveraging internet search data for improved early diagnosis and health care outcomes requires effectively addressing ethical, technical, and policy challenges. By fostering interdisciplinary collaboration, advancing infrastructure development, and prioritizing patient engagement and consent, researchers can unlock the transformative potential of internet search data in medical diagnosis to ultimately enhance patient care and advance health care practice and policy. ",
doi="10.2196/63149",
url="https://mental.jmir.org/2025/1/e63149",
url="http://www.ncbi.nlm.nih.gov/pubmed/39813106"
}


@Article{info:doi/10.2196/66446,
author="Zhang, Zhongmin
and Xu, Hengyi
and Pan, Jing
and Song, Fujian
and Chen, Ting",
title="Spatiotemporal Characteristics and Influential Factors of Electronic Cigarette Web-Based Attention in Mainland China: Time Series Observational Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="10",
volume="27",
pages="e66446",
keywords="electronic cigarettes",
keywords="Baidu index",
keywords="web-based attention",
keywords="spatiotemporal characteristics",
keywords="China",
abstract="Background: The popularity of electronic cigarettes (e-cigarettes) has steadily increased, prompting a considerable number of individuals to search for relevant information on them. Previous e-cigarette infodemiology studies have focused on assessing the quality and reliability of website content and quantifying the impact of policies. In reality, most low-income countries and low- and middle-income countries have not yet conducted e-cigarette use surveillance. Data sourced from web-based search engines related to e-cigarettes have the potential to serve as cost-effective supplementary means to traditional monitoring approaches. Objective: This study aimed to analyze the spatiotemporal distribution characteristics and associated sociodemographic factors of e-cigarette searches using trends from the Baidu search engine. Methods: The query data related to e-cigarettes for 31 provinces in mainland China were retrieved from the Baidu index database from January 1, 2015, to December 31, 2022. Concentration ratio methods and spatial autocorrelation analysis were applied to analyze the temporal aggregation and spatial aggregation of the e-cigarette Baidu index, respectively. A variance inflation factor test was performed to avoid multicollinearity. A spatial panel econometric model was developed to assess the determinants of e-cigarette web-based attention. Results: The daily average Baidu index for e-cigarettes increased from 53,234.873 in 2015 to 85,416.995 in 2021 and then declined to 52,174.906 in 2022. This index was concentrated in the southeastern coastal region, whereas the hot spot shifted to the northwestern region after adjusting for population size. Positive spatial autocorrelation existed in the per capita Baidu index of e-cigarettes from 2015 to 2022. The results of the local Moran's I showed that there were mainly low-low cluster areas of the per capita Baidu index, especially in the central region. Furthermore, the male-female ratio, the proportion of high school and above education, and the per capita gross regional domestic product were positively correlated with the per capita Baidu index for e-cigarettes. A higher urbanization rate was associated with a reduced per capita Baidu index. Conclusions: With the increasing popularity of web-based searches for e-cigarettes, a targeted e-cigarette health education program for individuals in the northwest, males, rural populations, high school and above educated individuals, and high-income groups is warranted. ",
doi="10.2196/66446",
url="https://www.jmir.org/2025/1/e66446",
url="http://www.ncbi.nlm.nih.gov/pubmed/39928402"
}


@Article{info:doi/10.2196/66072,
author="Xiong, Xin
and Xiang, Linghui
and Chang, Litao
and Wu, XY Irene
and Deng, Shuzhen",
title="Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="6",
volume="27",
pages="e66072",
keywords="mumps",
keywords="deep learning",
keywords="baidu index",
keywords="forecasting",
keywords="incidence prediction",
keywords="time series analysis",
keywords="Yunnan",
keywords="China",
abstract="Background: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. Objective: Our study aims to leverage the Baidu search index, representing search volumes from China's most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. Methods: We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. Results: The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3\% in 2015 to 45.7\% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 {\textmu}m or less, and particulate matter with a diameter of 10 {\textmu}m or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9\%, and 0.43, respectively, in the test set. Conclusions: Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks. ",
doi="10.2196/66072",
url="https://www.jmir.org/2025/1/e66072"
}


@Article{info:doi/10.2196/58338,
author="Kaushik, Aprajita
and Barcellona, Capucine
and Mandyam, Kanumoory Nikita
and Tan, Ying Si
and Tromp, Jasper",
title="Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="4",
volume="27",
pages="e58338",
keywords="artificial intelligence",
keywords="data sharing",
keywords="health care",
keywords="low- and middle-income countries",
keywords="AI tools",
keywords="systematic review",
keywords="case study",
keywords="Thailand",
keywords="computing machinery",
keywords="academic experts",
keywords="technology developers",
keywords="health care providers",
keywords="internet connectivity",
keywords="data systems",
keywords="low health data literacy",
keywords="cybersecurity",
keywords="standardized data formats",
keywords="AI development",
keywords="PRISMA",
abstract="Background: Health care systems in low- and middle-income countries (LMICs) can greatly benefit from artificial intelligence (AI) interventions in various use cases such as diagnostics, treatment, and public health monitoring but face significant challenges in sharing data for developing and deploying AI in health care. Objective: This study aimed to identify barriers and enablers to data sharing for AI in health care in LMICs and to test the relevance of these in a local context. Methods: First, we conducted a systematic literature search using PubMed, SCOPUS, Embase, Web of Science, and ACM using controlled vocabulary. Primary research studies, perspectives, policy landscape analyses, and commentaries performed in or involving an LMIC context were included. Studies that lacked a clear connection to health information exchange systems or were not reported in English were excluded from the review. Two reviewers independently screened titles and abstracts of the included articles and critically appraised each study. All identified barriers and enablers were classified according to 7 categories as per the predefined framework---technical, motivational, economic, political, legal and policy, ethical, social, organisational, and managerial. Second, we tested the local relevance of barriers and enablers in Thailand through stakeholder interviews with 15 academic experts, technology developers, regulators, policy makers, and health care providers. The interviewers took notes and analyzed data using framework analysis. Coding procedures were standardized to enhance the reliability of our approach. Coded data were reverified and themes were readjusted where necessary to avoid researcher bias. Results: We identified 22 studies, the majority of which were conducted across Africa (n=12, 55\%) and Asia (n=6, 27\%). The most important data-sharing challenges were unreliable internet connectivity, lack of equipment, poor staff and management motivation, uneven resource distribution, and ethical concerns. Possible solutions included improving IT infrastructure, enhancing funding, introducing user-friendly software, and incentivizing health care organizations and personnel to share data for AI-related tools. In Thailand, inconsistent data systems, limited staff time, low health data literacy, complex and unclear policies, and cybersecurity issues were important data-sharing challenges. Key solutions included building a conducive digital ecosystem---having shared data input platforms for health facilities to ensure data uniformity and to develop easy-to-understand consent forms, having standardized guidelines for data sharing, and having compensation policies for data breach victims. Conclusions: Although AI in LMICs has the potential to overcome health inequalities, these countries face technical, political, legal, policy, and organizational barriers to sharing data, which impede effective AI development and deployment. When tested in a local context, most of these barriers were relevant. Although our findings might not be generalizable to other contexts, this study can be used by LMICs as a framework to identify barriers and strengths within their health care systems and devise localized solutions for enhanced data sharing. Trial Registration: PROSPERO CRD42022360644; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=360644 ",
doi="10.2196/58338",
url="https://www.jmir.org/2025/1/e58338"
}


@Article{info:doi/10.2196/63449,
author="Hsu, Wan-Chen",
title="eHealth Literacy and Cyberchondria Severity Among Undergraduate Students: Mixed Methods Study",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="3",
volume="9",
pages="e63449",
keywords="eHealth literacy",
keywords="undergraduate student",
keywords="cyberchondria",
keywords="compucondria",
keywords="web-based health information",
keywords="health information seeking",
keywords="college students",
abstract="Background: With the development of the internet, health care websites have become increasingly important by enabling easy access to health information, thereby influencing the attitudes and behaviors of individuals toward health issues. However, few studies have addressed public access to health information and self-diagnosis. Objective: This study investigated the background factors and status of cyberchondria severity among college students by conducting a nationwide sample survey using the Cyberchondria Severity Scale. Further, we explored the perspective of eHealth literacy of those with scores higher than 1 SD from the mean by analyzing their recent experiences using web-based health information. Methods: A nationally representative sample of college students was surveyed, and 802 valid responses were obtained (male: 435/802, 54.2\%; female: 367/802, 45.8\%; mean age 20.3, SD 1.4 years). The Cyberchondria Severity Scale was used, which consisted of 4 dimensions (increased anxiety, obsessive-compulsive hypochondria, perceived controllability, and web-based physician-patient interaction). Additionally, we recruited 9 volunteers who scored more than 1 SD above the mean for in-depth interviews on their web-based health information--seeking behaviors. Results: Significant differences were found across the 4 dimensions of cyberchondria severity (F3,2403=256.26; P<.001), with perceived controllability scoring the highest (mean 2.75, SD 0.87) and obsessive-compulsive hypochondria scoring the lowest (mean 2.19, SD 0.77). Positive correlations were observed between perceived controllability, web-based physician-patient interactions, increased anxiety, and obsessive-compulsive hypochondria (r=0.46-0.75, P<.001). Regression analysis indicated that health concern significantly predicted perceived controllability ($\beta$ coefficient=0.12; P<.05) and web-based physician-patient interaction ($\beta$ coefficient=0.16; P<.001). Interview data revealed that students often experienced heightened anxiety (8/9, 89\%) and stress (7/9, 78\%) after exposure to web-based health information, highlighting the need for improved health literacy and reliable information sources. Conclusions: The study identified both benefits and risks in college students' use of web-based health information, emphasizing the importance of critical consciousness and eHealth literacy. Future research should examine how college students move from self-awareness to actionable change and the development of critical health literacy, which are essential for effective digital health engagement. ",
doi="10.2196/63449",
url="https://formative.jmir.org/2025/1/e63449"
}


@Article{info:doi/10.2196/55309,
author="Chan, J. Garrett
and Fung, Mark
and Warrington, Jill
and Nowak, A. Sarah",
title="Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis",
journal="JMIR Form Res",
year="2025",
month="Jan",
day="29",
volume="9",
pages="e55309",
keywords="digital health",
keywords="internet",
keywords="open data",
keywords="social networking",
keywords="social media",
abstract="Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice. Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions. Our goal was to characterize these topics and identify trends in these social media--based medical discussions. Methods: Using an initial query, we collected 1 year of Reddit posts containing the phrases ``get tested'' and ``get checked.'' These posts were manually reviewed, and subreddits containing irrelevant posts were excluded from analysis. This selection of posts was manually read by the investigators to categorize posts into topics. A script was developed to automatically assign topics to additional posts based on keywords. Topic and keyword selections were refined based on manual review for more accurate topic assignment. Topic assignment was then performed on the entire 1-year Reddit dataset containing 347,130 posts. Related topics were grouped into broader medical disciplines. Analysis of the topic assignments was then conducted to assess condition and medical topic frequencies in medical condition--focused subreddits and general subreddits. Results: We created an automated algorithm to assign medical topics to Reddit posts. By iterating through multiple rounds of topic assignment, we improved the accuracy of the algorithm. Ultimately, this algorithm created 82 topics sorted into 17 broader medical disciplines. Of all topics, sexually transmitted infections (STIs), eye disorders, anxiety, and pregnancy had the highest post frequency overall. STIs comprised 7.44\% (5876/78,980) of posts, and anxiety comprised 5.43\% (4289/78,980) of posts. A total of 34\% (28/82) of the topics comprised 80\% (63,184/78,980) of all posts. Of the medical disciplines, those with the most posts were?psychiatry and mental health;?genitourinary and reproductive health; infectious diseases;?and endocrinology, nutrition, and metabolism. Psychiatry and mental health comprised 26.6\% (21,009/78,980) of posts, and genitourinary and reproductive health comprised 13.6\% (10,741/78,980) of posts. Overall, most posts were also classified under these 4 medical disciplines. During analysis, subreddits were also classified as general if they did not focus on a specific health issue and topic-specific if they discussed a specific medical issue. Topics that appeared most frequently in the top 5 in general subreddits included addiction and drug anxiety, attention-deficit/hyperactivity disorder, abuse, and STIs. In topic-specific subreddits, most posts were found to discuss the topic of that subreddit. Conclusions: Certain health topics and medical disciplines are predominant on Reddit. These include topics such as STIs, eye disorders, anxiety, and pregnancy. Most posts were classified under the medical disciplines of psychiatry and mental health, as well as genitourinary and reproductive health. ",
doi="10.2196/55309",
url="https://formative.jmir.org/2025/1/e55309",
url="http://www.ncbi.nlm.nih.gov/pubmed/39879094"
}


@Article{info:doi/10.2196/69554,
author="Gu, Chenyu
and Qian, Liquan
and Zhuo, Xiaojie",
title="Mindfulness Intervention for Health Information Avoidance in Older Adults: Mixed Methods Study",
journal="JMIR Public Health Surveill",
year="2025",
month="Jan",
day="28",
volume="11",
pages="e69554",
keywords="health information avoidance",
keywords="cyberchondria",
keywords="self-determination theory",
keywords="mindfulness",
keywords="elderly",
abstract="Background: The global aging population and rapid development of digital technology have made health management among older adults an urgent public health issue. The complexity of online health information often leads to psychological challenges, such as cyberchondria, exacerbating health information avoidance behaviors. These behaviors hinder effective health management; yet, little research examines their mechanisms or intervention strategies. Objective: This study investigates the mechanisms influencing health information avoidance among older adults, emphasizing the mediating role of cyberchondria. In addition, it evaluates the effectiveness of mindfulness meditation as an intervention strategy to mitigate these behaviors. Methods: A mixed methods approach was used, combining quantitative and qualitative methodologies. Substudy 1 developed a theoretical model based on self-determination theory to explore internal (positive metacognition and health self-efficacy) and external (subjective norms and health information similarity) factors influencing health information avoidance, with cyberchondria as a mediator. A cross-sectional survey (N=236) was conducted to test the proposed model. Substudy 2 involved a 4-week mindfulness meditation intervention (N=94) to assess its impact on reducing health information avoidance behaviors. Results: Study 1 showed that positive metacognition ($\beta$=.26, P=.002), health self-efficacy ($\beta$=.25, P<.001), and health information similarity ($\beta$=.29, P<.001) significantly predicted health information avoidance among older adults. Cyberchondria mediated these effects: positive metacognition (effect=0.106, 95\% CI 0.035-0.189), health self-efficacy (effect=0.103, 95\% CI 0.043-0.185), and health information similarity (effect=0.120, 95\% CI 0.063-0.191). Subjective norms did not significantly predict health information avoidance ($\beta$=?.11, P=.13), and cyberchondria did not mediate this relationship (effect=?0.045, 95\% CI ?0.102 to 0.016). Study 2 found that after the 4-week mindfulness intervention, the intervention group (group 1: n=46) exhibited significantly higher mindfulness levels than the control group (group 2: n=48; Mgroup1=4.122, Mgroup2=3.606, P<.001) and higher levels compared with preintervention (Mt2=4.122, Mt1=3.502, P<.001, where t1=preintervention and t2=postintervention). However, cyberchondria levels did not change significantly (Mt1=2.848, Mt2=2.685, P=.18). Nevertheless, the results revealed a significant interaction effect between mindfulness and cyberchondria on health information avoidance (effect=?0.357, P=.002, 95\% CI ?0.580 to ?0.131), suggesting that mindfulness intervention effectively inhibited the transformation of cyberchondria into health information avoidance behavior. Conclusions: This study reveals the role of cyberchondria in health information avoidance and validates mindfulness meditation as an effective intervention for mitigating such behaviors. Findings offer practical recommendations for improving digital health information delivery and health management strategies for older adults. ",
doi="10.2196/69554",
url="https://publichealth.jmir.org/2025/1/e69554"
}


@Article{info:doi/10.2196/67192,
author="Scherbakov, A. Dmitry
and Hubig, C. Nina
and Lenert, A. Leslie
and Alekseyenko, V. Alexander
and Obeid, S. Jihad",
title="Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review",
journal="JMIR Ment Health",
year="2025",
month="Jan",
day="16",
volume="12",
pages="e67192",
keywords="natural language processing",
keywords="datasets",
keywords="mental health",
keywords="automated review",
keywords="depression",
keywords="suicide",
keywords="mental health research",
keywords="NLP",
keywords="artificial intelligence",
keywords="AI",
keywords="scoping review",
keywords="determinant",
keywords="large language model",
keywords="LLM",
keywords="quantitative",
keywords="automation",
abstract="Background: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated. Objective: This review aims to summarize the use of NLP in mental health research, with a special focus on the types of text datasets and the use of social determinants of health (SDOH) in NLP projects related to mental health. Methods: The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete. All citations were uploaded to Covidence (Veritas Health Innovation) software. The screening and extraction process took place in Covidence with the help of a custom large language model (LLM) module developed by our team. This LLM module was calibrated and tuned to automate many aspects of the review process. Results: The screening process, assisted by the custom LLM, led to the inclusion of 1768 studies in the final review. Most of the reviewed studies (n=665, 42.8\%) used clinical data as their primary text dataset, followed by social media datasets (n=523, 33.7\%). The United States contributed the highest number of studies (n=568, 36.6\%), with depression (n=438, 28.2\%) and suicide (n=240, 15.5\%) being the most frequently investigated mental health issues. Traditional demographic variables, such as age (n=877, 56.5\%) and gender (n=760, 49\%), were commonly extracted, while SDOH factors were less frequently reported, with urban or rural status being the most used (n=19, 1.2\%). Over half of the citations (n=826, 53.2\%) did not provide clear information on dataset accessibility, although a sizable number of studies (n=304, 19.6\%) made their datasets publicly available. Conclusions: This scoping review underscores the significant role of clinical notes and social media in NLP-based mental health research. Despite the clear relevance of SDOH to mental health, their underutilization presents a gap in current research. This review can be a starting point for researchers looking for an overview of mental health projects using text data. Shared datasets could be used to place more emphasis on SDOH in future studies. ",
doi="10.2196/67192",
url="https://mental.jmir.org/2025/1/e67192"
}


@Article{info:doi/10.2196/59625,
author="Huijgens, Fiorella
and Kwakman, Pascale
and Hillen, Marij
and van Weert, Julia
and Jaspers, Monique
and Smets, Ellen
and Linn, Annemiek",
title="How Patients With Cancer Use the Internet to Search for Health Information: Scenario-Based Think-Aloud Study",
journal="JMIR Infodemiology",
year="2025",
month="Jan",
day="16",
volume="5",
pages="e59625",
keywords="web-based health information seeking",
keywords="think aloud",
keywords="scenario based",
keywords="cancer",
keywords="patient evaluation",
keywords="information seeking",
keywords="web-based information",
keywords="health information",
keywords="internet",
keywords="pattern",
keywords="motivation",
keywords="cognitive",
keywords="emotional",
keywords="response",
keywords="patient",
keywords="survivor",
keywords="caregiver",
keywords="interview",
keywords="scenario",
keywords="women",
keywords="men",
abstract="Background: Patients with cancer increasingly use the internet to seek health information. However, thus far, research treats web-based health information seeking (WHIS) behavior in a rather dichotomous manner (ie, approaching or avoiding) and fails to capture the dynamic nature and evolving motivations that patients experience when engaging in WHIS throughout their disease trajectory. Insights can be used to support effective patient-provider communication about WHIS and can lead to better designed web-based health platforms. Objective: This study explored patterns of motivations and emotions behind the web-based information seeking of patients with cancer at various stages of their disease trajectory, as well as the cognitive and emotional responses evoked by WHIS via a scenario-based, think-aloud approach. Methods: In total, 15 analog patients were recruited, representing patients with cancer, survivors, and informal caregivers. Imagining themselves in 3 scenarios---prediagnosis phase (5/15, 33\%), treatment phase (5/15, 33\%), and survivor phase (5/15, 33\%)---patients were asked to search for web-based health information while being prompted to verbalize their thoughts. In total, 2 researchers independently coded the sessions, categorizing the codes into broader themes to comprehend analog patients' experiences during WHIS. Results: Overarching motives for WHIS included reducing uncertainty, seeking reassurance, and gaining empowerment. At the beginning of the disease trajectory, patients mainly showed cognitive needs, whereas this shifted more toward affective needs in the subsequent disease stages. Analog patients' WHIS approaches varied from exploratory to focused or a combination of both. They adapted their search strategy when faced with challenging cognitive or emotional content. WHIS triggered diverse emotions, fluctuating throughout the search. Complex, confrontational, and unexpected information mainly induced negative emotions. Conclusions: This study provides valuable insights into the motivations of patients with cancer underlying WHIS and the emotions experienced at various stages of the disease trajectory. Understanding patients' search patterns is pivotal in optimizing web-based health platforms to cater to specific needs. In addition, these findings can guide clinicians in accommodating patients' specific needs and directing patients toward reliable sources of web-based health information. ",
doi="10.2196/59625",
url="https://infodemiology.jmir.org/2025/1/e59625"
}


@Article{info:doi/10.2196/54460,
author="Weidinger, Florian
and Dietzel, Nikolas
and Graessel, Elmar
and Prokosch, Hans-Ulrich
and Kolominsky-Rabas, Peter",
title="Using Health Information Resources for People With Cognitive Impairment (digiDEM Bayern): Registry-Based Cohort Study",
journal="JMIR Form Res",
year="2025",
month="Jan",
day="15",
volume="9",
pages="e54460",
keywords="dementia",
keywords="mild cognitive impairment",
keywords="cognitive impairment",
keywords="information sources",
keywords="health information",
keywords="health information--seeking behavior",
keywords="Digital Dementia Registry Bavaria",
keywords="digiDEM",
abstract="Background: Dementia is a growing global health challenge with significant economic and social implications. Underdiagnosis of dementia is prevalent due to a lack of knowledge and understanding among the general population. Enhancing dementia literacy through improved health information--seeking behavior is crucial for the self-determined management of the disease by those affected. Understanding the relationship between dementia literacy, health information--seeking behavior, and the use of various information sources among individuals with cognitive impairment is of high importance in this context. Objective: The aim of this study was to analyze the relevance of different sources of health information from the perspective of people with cognitive impairment, while also evaluating differences based on age, gender, and disease progression. Methods: This study is part of the ongoing project ``Digital Dementia Registry Bavaria -- digiDEM Bayern.'' The Digital Dementia Registry Bavaria is a multicenter, prospective, longitudinal register study in Bavaria, Germany. People with cognitive impairment rated several information sources by using Likert scales with the values unimportant (1) to very important (5). Data were analyzed descriptively, and multiple 2-sample, 2-tailed t tests were used to evaluate differences by cognitive status and gender and using multiple one-way ANOVA to evaluate differences by age group. Results: Data of 924 people with cognitive impairment (531 with dementia, 393 with mild cognitive impairment) were evaluated. The most relevant health information sources were ``Personal visit to a medical professional'' (mean 3.9, SD 1.1) and ``Family / Friends'' (mean 3.9, SD 1.2). ``Internet'' was 1 of the 2 lowest-rated information sources by people with cognitive impairment (mean 1.6, SD 1.1), with nearly three-quarters (684/924, 74\%) of the participants rating the source as unimportant. The age-specific analyses showed significant differences for the sources ``Internet'' (F2,921=61.23; P<.001), ``Courses / Lectures'' (F2,921=18.88; P<.001), and ``Family / Friends'' (F2,921=6.27; P=.002) for the 3 defined age groups. There were several significant differences between people with mild cognitive impairment and dementia whereby the first group evaluated most sources higher, such as ``Internet'' (mean difference=0.6; t640=7.52; P<.001). The only sources rated higher by the dementia group were ``TV / Radio'' and ``Family / Friends,'' with none of them showing significant differences. Gender-specific analyses showed women with cognitive impairment valuing every evaluated source higher than men apart from ``Internet'' (mean difference=0.4; t685=4.97; P<.001). Conclusions: To enhance health and dementia literacy, the best way to communicate health information to people with cognitive impairment is through interpersonal contact with medical professionals and their friends and family. Slight changes in valuation should be considered as the medical condition progresses, along with variations by age and gender. In particular, the evaluation and use of the internet are dependent on these factors. Further research is needed to capture potential changes in the valuation of the internet as a health information source. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2020-043473 ",
doi="10.2196/54460",
url="https://formative.jmir.org/2025/1/e54460"
}


@Article{info:doi/10.2196/50862,
author="Lang, A. Iain
and King, Angela
and Boddy, Kate
and Stein, Ken
and Asare, Lauren
and Day, Jo
and Liabo, Kristin",
title="Jargon and Readability in Plain Language Summaries of Health Research: Cross-Sectional Observational Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="13",
volume="27",
pages="e50862",
keywords="readability",
keywords="jargon",
keywords="reading",
keywords="accessibility",
keywords="health research",
keywords="science communication",
keywords="public understanding of science",
keywords="open science",
keywords="patient and public involvement",
keywords="health literacy",
keywords="plain language summary",
keywords="health communication",
abstract="Background: The idea of making science more accessible to nonscientists has prompted health researchers to involve patients and the public more actively in their research. This sometimes involves writing a plain language summary (PLS), a short summary intended to make research findings accessible to nonspecialists. However, whether PLSs satisfy the basic requirements of accessible language is unclear. Objective: We aimed to assess the readability and level of jargon in the PLSs of research funded by the largest national clinical research funder in Europe, the United Kingdom's National Institute for Health and Care Research (NIHR). We also aimed to assess whether readability and jargon were influenced by internal and external characteristics of research projects. Methods: We downloaded the PLSs of all NIHR National Journals Library reports from mid-2014 to mid-2022 (N=1241) and analyzed them using the Flesch Reading Ease (FRE) formula and a jargon calculator (the De-Jargonizer). In our analysis, we included the following study characteristics of each PLS: research topic, funding program, project size, length, publication year, and readability and jargon scores of the original funding proposal. Results: Readability scores ranged from 1.1 to 70.8, with an average FRE score of 39.0 (95\% CI 38.4-39.7). Moreover, 2.8\% (35/1241) of the PLSs had an FRE score classified as ``plain English'' or better; none had readability scores in line with the average reading age of the UK population. Jargon scores ranged from 76.4 to 99.3, with an average score of 91.7 (95\% CI 91.5-91.9) and 21.7\% (269/1241) of the PLSs had a jargon score suitable for general comprehension. Variables such as research topic, funding program, and project size significantly influenced readability and jargon scores. The biggest differences related to the original proposals: proposals with a PLS in their application that were in the 20\% most readable were almost 3 times more likely to have a more readable final PLS (incidence rate ratio 2.88, 95\% CI 1.86-4.45). Those with the 20\% least jargon in the original application were more than 10 times as likely to have low levels of jargon in the final PLS (incidence rate ratio 13.87, 95\% CI 5.17-37.2). There was no observable trend over time. Conclusions: Most of the PLSs published in the NIHR's National Journals Library have poor readability due to their complexity and use of jargon. None were readable at a level in keeping with the average reading age of the UK population. There were significant variations in readability and jargon scores depending on the research topic, funding program, and other factors. Notably, the readability of the original funding proposal seemed to significantly impact the final report's readability. Ways of improving the accessibility of PLSs are needed, as is greater clarity over who and what they are for. ",
doi="10.2196/50862",
url="https://www.jmir.org/2025/1/e50862"
}


@Article{info:doi/10.2196/58457,
author="Wals Zurita, Jesus Amadeo
and Miras del Rio, Hector
and Ugarte Ruiz de Aguirre, Nerea
and Nebrera Navarro, Cristina
and Rubio Jimenez, Maria
and Mu{\~n}oz Carmona, David
and Miguez Sanchez, Carlos",
title="The Transformative Potential of Large Language Models in Mining Electronic Health Records Data: Content Analysis",
journal="JMIR Med Inform",
year="2025",
month="Jan",
day="2",
volume="13",
pages="e58457",
keywords="electronic health record",
keywords="EHR",
keywords="oncology",
keywords="radiotherapy",
keywords="data mining",
keywords="ChatGPT",
keywords="large language models",
keywords="LLMs",
abstract="Background: In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. Objective: We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. Methods: We implemented a script using the OpenAI application programming interface to extract structured information in JavaScript object notation format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by 5 specialists in radiation oncology. We compared the results using metrics such as sensitivity, specificity, precision, accuracy, F-value, $\kappa$ index, and the McNemar test, in addition to examining the common causes of errors in both humans and generative pretrained transformer (GPT) models. Results: The GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar test, P=.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar test, P<.001). Notably, it achieved a sensitivity of 96.8\%, compared to 88.2\% for GPT-3.5 and 88.8\% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7\% vs 96.8\%). GPT-4 showed greater consistency, replicating the exact same results in 76\% of the reports across 10 repeated analyses, compared to 59\% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred nonexplicit comorbidities, sometimes correctly, though this also resulted in more false positives. Conclusions: This study demonstrates that, with well-designed prompts, the large language models examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation. ",
doi="10.2196/58457",
url="https://medinform.jmir.org/2025/1/e58457"
}


@Article{info:doi/10.2196/64577,
author="Lemieux, Mackenzie
and Zhou, Cyrus
and Cary, Caroline
and Kelly, Jeannie",
title="Changes in Reproductive Health Information-Seeking Behaviors After the Dobbs Decision: Systematic Search of the Wikimedia Database",
journal="JMIR Infodemiology",
year="2024",
month="Dec",
day="16",
volume="4",
pages="e64577",
keywords="abortion",
keywords="Dobbs",
keywords="internet",
keywords="viewer trends",
keywords="Wikipedia",
keywords="women's health",
keywords="contraception",
keywords="contraceptive",
keywords="trend",
keywords="information seeking",
keywords="page view",
keywords="reproductive",
keywords="reproduction",
abstract="Background: After the US Supreme Court overturned Roe v. Wade, confusion followed regarding the legality of abortion in different states across the country. Recent studies found increased Google searches for abortion-related terms in restricted states after the Dobbsv. Jackson Women's Health Organization decision was leaked. As patients and providers use Wikipedia (Wikimedia Foundation) as a predominant medical information source, we hypothesized that changes in reproductive health information-seeking behavior could be better understood by examining Wikipedia article traffic. Objective: This study aimed to examine trends in Wikipedia usage for abortion and contraception information before and after the Dobbs decision. Methods: Page views of abortion- and contraception-related Wikipedia pages were scraped. Temporal changes in page views before and after the Dobbs decision were then analyzed to explore changes in baseline views, differences in views for abortion-related information in states with restrictive abortion laws versus nonrestrictive states, and viewer trends on contraception-related pages. Results: Wikipedia articles related to abortion topics had significantly increased page views following the leaked and final Dobbs decision. There was a 103-fold increase in the page views for the Wikipedia article Roe v. Wade following the Dobbs decision leak (mean 372,654, SD 135,478 vs mean 3614, SD 248; P<.001) and a 67-fold increase in page views following the release of the final Dobbs decision (mean 8942, SD 402 vs mean 595,871, SD 178,649; P<.001). Articles about abortion in the most restrictive states had a greater increase in page views (mean 40.6, SD 12.7; 18/51, 35\% states) than articles about abortion in states with some restrictions or protections (mean 26.8, SD 7.3; 24/51, 47\% states; P<.001) and in the most protective states (mean 20.6, SD 5.7; 8/51, 16\% states; P<.001). Finally, views to pages about common contraceptive methods significantly increased after the Dobbs decision. ``Vasectomy'' page views increased by 183\% (P<.001), ``IUD'' (intrauterine device) page views increased by 80\% (P<.001), ``Combined oral contraceptive pill'' page views increased by 24\% (P<.001), ``Emergency Contraception'' page views increased by 224\% (P<.001), and ``Tubal ligation'' page views increased by 92\% (P<.001). Conclusions: People sought information on Wikipedia about abortion and contraception at increased rates after the Dobbs decision. Increased traffic to abortion-related Wikipedia articles correlated to the restrictiveness of state abortion policies. Increased interest in contraception-related pages reflects the increased demand for contraceptives observed after the Dobbs decision. Our work positions Wikipedia as an important source of reproductive health information and demands increased attention to maintain and improve Wikipedia as a reliable source of health information after the Dobbs decision. ",
doi="10.2196/64577",
url="https://infodemiology.jmir.org/2024/1/e64577"
}


@Article{info:doi/10.2196/63476,
author="Ahn, Seong-Ho
and Yim, Kwangil
and Won, Hyun-Sik
and Kim, Kang-Min
and Jeong, Dong-Hwa",
title="Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study",
journal="J Med Internet Res",
year="2024",
month="Dec",
day="16",
volume="26",
pages="e63476",
keywords="COVID-19",
keywords="confirmed case prediction",
keywords="search engine queries",
keywords="query expansion",
keywords="word embedding",
keywords="public health",
keywords="case prediction",
keywords="South Korea",
keywords="search engine",
keywords="infodemiology",
keywords="infodemiology study",
keywords="policy",
keywords="lifestyle",
keywords="machine learning",
keywords="machine learning techniques",
keywords="utilization",
keywords="temporal variation",
keywords="novel framework",
keywords="temporal",
keywords="web-based search",
keywords="temporal semantics",
keywords="prediction model",
keywords="model",
abstract="Background: The number of confirmed COVID-19 cases is a crucial indicator of policies and lifestyles. Previous studies have attempted to forecast cases using machine learning techniques that use a previous number of case counts and search engine queries predetermined by experts. However, they have limitations in reflecting temporal variations in queries associated with pandemic dynamics. Objective: This study aims to propose a novel framework to extract keywords highly associated with COVID-19, considering their temporal occurrence. We aim to extract relevant keywords based on pandemic variations using query expansion. Additionally, we examine time-delayed web-based search behavior related to public interest in COVID-19 and adjust for better prediction performance. Methods: To capture temporal semantics regarding COVID-19, word embedding models were trained on a news corpus, and the top 100 words related to ``Corona'' were extracted over 4-month windows. Time-lagged cross-correlation was applied to select optimal time lags correlated to confirmed cases from the expanded queries. Subsequently, ElasticNet regression models were trained after reducing the feature dimensions using principal component analysis of the time-lagged features to predict future daily case counts. Results: Our approach successfully extracted relevant keywords depending on the pandemic phase, encompassing keywords directly related to COVID-19, such as its symptoms, and its societal impact. Specifically, during the first outbreak, keywords directly linked to COVID-19 and past infectious disease outbreaks similar to those of COVID-19 exhibited a high positive correlation. In the second phase of the pandemic, as community infections emerged, keywords related to the government's pandemic control policies were frequently observed with a high positive correlation. In the third phase of the pandemic, during the delta variant outbreak, keywords such as ``economic crisis'' and ``anxiety'' appeared, reflecting public fatigue. Consequently, prediction models trained by the extracted queries over 4-month windows outperformed previous methods for most predictions 1-14 days ahead. Notably, our approach showed significantly higher Pearson correlation coefficients than models based solely on the number of past cases for predictions 9-11 days ahead (P=.02, P<.01, and P<.01), in contrast to heuristic- and symptom-based query sets. Conclusions: This study proposes a novel COVID-19 case-prediction model that automatically extracts relevant queries over time using word embedding. The model outperformed previous methods that relied on static symptom-based or heuristic queries, even without prior expert knowledge. The results demonstrate the capability of our approach to track temporal shifts in public interest regarding changes in the pandemic. ",
doi="10.2196/63476",
url="https://www.jmir.org/2024/1/e63476"
}


@Article{info:doi/10.2196/58482,
author="Ageng, Kidung
and Inthiran, Anushia",
title="Exploring Pregnancy-Related Information-Sharing Behavior Among First-Time Southeast Asian Fathers: Qualitative Semistructured Interview Study",
journal="JMIR Pediatr Parent",
year="2024",
month="Dec",
day="9",
volume="7",
pages="e58482",
keywords="pregnancy",
keywords="first-time fathers",
keywords="information sharing",
keywords="Southeast Asia",
keywords="information-seeking behavior",
keywords="cultural factors",
abstract="Background: While the benefits of fathers' engagement in pregnancy are well researched, little is known about first-time expectant fathers' information-seeking practices in Southeast Asia regarding pregnancy. In addition, there is a notable gap in understanding their information-sharing behaviors during the pregnancy journey. This information is important, as cultural norms are prevalent in Southeast Asia, and this might influence their information-sharing behavior, particularly about pregnancy. Objective: This study aims to explore and analyze the pregnancy-related information-sharing behavior of first-time expectant fathers in Southeast Asia. This study specifically aims to investigate whether first-time fathers share pregnancy information, with whom they share it, through what means, and the reasons behind the decisions to share the information or not. Methods: We conducted semistructured interviews with first-time Southeast Asian fathers in Indonesia, a sample country in the Southeast Asian region. We analyzed the data using quantitative descriptive analysis and qualitative content theme analysis. A total of 40 first-time expectant fathers were interviewed. Results: The results revealed that 90\% (36/40) of the participants shared pregnancy-related information with others. However, within this group, more than half (22/40, 55\%) of the participants shared the information exclusively with their partners. Only a small proportion, 10\% (4/40), did not share any information at all. Among those who did share, the most popular approach was face-to-face communication (36/40, 90\%), followed by online messaging apps (26/40, 65\%). The most popular reason for sharing was to validate information (14/40, 35\%), while the most frequent reason for not sharing with anyone beyond their partner was because of the preference for asking for information rather than sharing (12/40, 30\%). Conclusions: This study provides valuable insights into the pregnancy-related information-sharing behaviors of first-time fathers in Southeast Asia. It enhances our understanding of how first-time fathers share pregnancy-related information and how local cultural norms and traditions influence these practices. In contrast to first-time fathers in high-income countries, the information-sharing behavior of first-time Southeast Asian fathers is defined by cultural nuances. Culture plays a crucial role in their daily decision-making processes. Therefore, this emphasizes the importance of cultural considerations in future discussions and the development of intervention programs related to pregnancy for first-time Southeast Asian fathers. In addition, this study sheds light on the interaction processes that first-time fathers engage in with others, highlighting areas where intervention programs may be necessary to improve their involvement during pregnancy. For example, first-time fathers actively exchange new information found with their partners; therefore, creating features or platforms that facilitate this process could improve their overall experience. Furthermore, health practitioners should take a more proactive approach in engaging with first-time fathers, as currently there is a communication gap between them. ",
doi="10.2196/58482",
url="https://pediatrics.jmir.org/2024/1/e58482"
}


@Article{info:doi/10.2196/54092,
author="Na, Kilhoe
and Zimdars, Melissa
and Cullinan, E. Megan",
title="Understanding Membership in Alternative Health Social Media Groups and Its Association with COVID-19 and Influenza Vaccination: Web-Based Cross-Sectional Survey",
journal="JMIR Form Res",
year="2024",
month="Dec",
day="5",
volume="8",
pages="e54092",
keywords="alternative health",
keywords="social media",
keywords="misinformation",
keywords="vaccination",
keywords="COVID-19",
keywords="Coronavirus",
abstract="Background: Social media platforms have become home to numerous alternative health groups where people share health information and scientifically unproven treatments. Individuals share not only health information but also health misinformation in alternative health groups on social media. Yet, little research has been carried out to understand members of these groups. This study aims to better understand various characteristics of members in alternative health groups and the association between membership and attitudes toward vaccination and COVID-19 and influenza vaccination--related behaviors. Objective: This study aims to test hypotheses about different potential characteristics of members in alternative health groups and the association between membership and attitudes toward vaccination and vaccine-related behaviors. Methods: A web-based cross-sectional survey (N=1050) was conducted. Participants were recruited from 19 alternative health social media groups and Amazon's Mechanical Turk. A total of 596 participants were members of alternative health groups and 454 were nonmembers of alternative health groups. Logistic regressions were performed to test the hypotheses about the relationship between membership and the variables of interest. Results: Logistic regression revealed that there is a positive association between alternative health social media group membership and 3 personal characteristics: sharing trait (B=.83, SE=.11; P<.01; odds ratio [OR] 2.30, 95\% CI 1.85-2.86), fear of negative evaluations (B=.19, SE=.06; P<.001, OR 1.21, 95\% CI 1.06-1.37), and conspiratorial mentality (B=.33, SE=.08; P<.01; OR 1.40, 95\% CI 1.18-1.65). Also, the results indicate that there is a negative association between membership and 2 characteristics: health literacy (B=--1.09, SE=.17; P<.001; OR .33, 95\% CI 0.23-0.47) and attitudes toward vaccination (B=-- 2.33, SE=.09; P=.02; OR 0.79, 95\% CI 0.65-0.95). However, there is no association between membership and health consciousness (B=.12, SE=.10; P=.24; OR 1.13, 95\% CI 0.92-1.38). Finally, membership is negatively associated with COVID-19 vaccination status (B=--.84, SE=.17; P<.001; OR 48, 95\% CI 0.32-0.62), and influenza vaccination practice (B=--1.14, SE=.17; P<.001; OR .31, 95\% CI 0.22-0.45). Conclusions: Our findings indicate that people joining alternative health social media groups differ from nonmembers in different aspects, such as sharing, fear of negative evaluations, conspiratorial mentality, and health literacy. They also suggest that there is a significant relationship between membership and vaccination. By more thoroughly exploring the demographic, or by better understanding the people for whom interventions are designed, this study is expected to help researchers to more strategically and effectively develop and implement interventions. ",
doi="10.2196/54092",
url="https://formative.jmir.org/2024/1/e54092"
}


@Article{info:doi/10.2196/63281,
author="Gaba, Ann
and Bennett, Richard",
title="Health-Related Messages About Herbs, Spices, and Other Botanicals Appearing in Print Issues and Websites of Legacy Media: Content Analysis and Evaluation",
journal="JMIR Form Res",
year="2024",
month="Dec",
day="4",
volume="8",
pages="e63281",
keywords="legacy media",
keywords="health applications",
keywords="health communication",
keywords="botanical products",
keywords="content analysis",
abstract="Background: Legacy media are publications that existed before the internet. Many of these have migrated to a web format, either replacing or in parallel to their print issues. Readers place an economic value on access to the information presented as they pay for subscriptions and place a higher degree of trust in their content. Much has been written about inaccurate and misleading health information in social media; however, the content and accuracy of information contained in legacy media has not been examined in detail. Discussion of herbs, spices, and other botanicals has been absent from this context. Objective: The objectives of this study were to (1) identify the health associations of botanical products mentioned in legacy media targeted to a range of demographic groups and (2) evaluate these health associations for accuracy against published scientific studies. Methods: In total, 10 popular magazines targeting a range of gender, race/ethnicity, and sexual orientation demographic groups were selected for analysis. Relevant content was extracted and coded over 1 year. Associations between specific botanical products and health factors were identified. For the most frequent botanical--health application associations, a PubMed search was conducted to identify reviews corresponding to each item's indicated applications. Where no systematic reviews were available, single research studies were sought. Results: A total of 237 unique botanical products were identified. There were 128 mentions of these in the print issues and 1215 on the websites. In total, 18 health applications were identified and used to categorize the indicated uses for the various products individually and as general categories. The most frequently mentioned applications were skin care, with 913 mentions, immunity enhancement, with 705 mentions, gastrointestinal health and probiotics, with 184 mentions, and cognitive function (stress and mental health), with 106 mentions. Comparison to published literature evaluating the efficacy of these functions identified positive support for aloe vera, argan oil, chamomile, jojoba oil, lavender, rosemary, and tea tree oil in skin care. Berries, ginger, turmeric, and green tea had the strongest evidence for a role in immunity enhancement. Ginger and oats were supported as having a role in gastrointestinal health. Finally, berries, lavender, ashwagandha, and cannabidiol were supported as having a role in managing stress. Other frequently mentioned items such as aloe vera, ashwagandha, or mushrooms for immunity were less strongly supported. Conclusions: Comparison of the most prevalent associations between botanical products and health applications to published literature indicates that, overall, these associations were consistent with current scientific reports about the health applications of botanical products. While some products had a greater degree of research support than others, truly egregious falsehoods were absent. Therefore, legacy media may be considered a credible source of information to readers about these topics. ",
doi="10.2196/63281",
url="https://formative.jmir.org/2024/1/e63281"
}


@Article{info:doi/10.2196/57718,
author="Jagomast, Tobias
and Finck, Jule
and Tangemann-M{\"u}nstedt, Imke
and Auth, Katharina
and Dr{\"o}mann, Daniel
and Franzen, F. Klaas",
title="Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis",
journal="Online J Public Health Inform",
year="2024",
month="Dec",
day="3",
volume="16",
pages="e57718",
keywords="internet",
keywords="coronavirus",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="pandemics",
keywords="public health",
keywords="smoking cessation",
keywords="tobacco products",
keywords="Google Trends",
keywords="relative search volume",
keywords="Europe",
keywords="online",
keywords="search",
keywords="smoking",
keywords="addiction",
keywords="quit",
keywords="cessation",
keywords="trend",
keywords="cluster",
keywords="public interest",
keywords="lockdown",
keywords="vaccination",
keywords="spread",
keywords="incidence",
abstract="Background: Smoking is a modifiable risk factor for SARS-CoV-2 infection. Evidence of smoking behavior during the pandemic is ambiguous. Most investigations report an increase in smoking. In this context, Google Trends data monitor real-time public information--seeking behavior and are therefore useful to characterize smoking-related interest over the trajectory of the pandemic. Objective: This study aimed to use Google Trends data to evaluate the effect of the pandemic on public interest in smoking-related topics with a focus on lockdowns, vaccination campaigns, and incidence. Methods: The weekly relative search volume was retrieved from Google Trends for England, Germany, Italy, and Spain from December 31, 2017, to April 18, 2021. Data were collected for keywords concerning consumption, cessation, and treatment. The relative search volume before and during the pandemic was compared, and general trends were evaluated using the Wilcoxon rank-sum test. Short-term changes and hereby temporal clusters linked to lockdowns or vaccination campaigns were addressed by the flexible spatial scan statistics proposed by Takahashi and colleagues. Subsequently, the numbers of clusters after the onset of the pandemic were compared by chi-square test. Results: Country-wise minor differences were observed while 3 overarching trends prevailed. First, regarding cessation, the statistical comparison revealed a significant decline in interest for 58\% (7/12) of related keywords, and fewer clusters were present during the pandemic. Second, concerning consumption, significantly reduced relative search volume was observed for 58\% (7/12) of keywords, while treatment-related keywords exhibited heterogeneous trends. Third, substantial clusters of increased interest were sparsely linked to lockdowns, vaccination campaigns, or incidence. Conclusions: This study reports a substantial decline in overall relative search volume and clusters for cessation interest. These results underline the importance of intensifying cessation aid during times of crisis. Lockdowns, vaccination, and incidence had less impact on information-seeking behavior. Other public measures that positively affect smoking behavior remain to be determined. ",
doi="10.2196/57718",
url="https://ojphi.jmir.org/2024/1/e57718",
url="http://www.ncbi.nlm.nih.gov/pubmed/39626237"
}


@Article{info:doi/10.2196/53440,
author="Blank, Ann Carol
and Biedka, Sarah
and Montalmant, Abigail
and Saft, Katelyn
and Lape, Miranda
and Mao, Kate
and Bradt, Joke
and Liou, T. Kevin",
title="Scope, Findability, and Quality of Information About Music-Based Interventions in Oncology: Quantitative Content Analysis of Public-Facing Websites at National Cancer Institute--Designated Cancer Centers",
journal="JMIR Cancer",
year="2024",
month="Nov",
day="22",
volume="10",
pages="e53440",
keywords="music-based interventions",
keywords="cancer",
keywords="oncology",
keywords="symptom management",
keywords="music therapy",
keywords="music services",
keywords="National Cancer Institute",
abstract="Background: Music-based interventions (MBIs) are evidence-based, nonpharmacological treatments that include music therapy (MT) delivered by board-certified music therapists, as well as music services (MS) delivered by other health professionals and volunteers. Despite MBI's growing evidence base in cancer symptom management, it remains unclear how MBI-related information is presented to the public. Over 80\% of people with cancer use the internet to find health-related information. In the United States, the National Cancer Institute (NCI) identifies certain Cancer Centers (CCs) as NCI-designated CCs or Comprehensive Cancer Centers (CCCs) based on their excellence in research. As NCI-designated CCs and CCCs are considered the gold standard in cancer care, their websites are viewed by the public as important sources of information. Objective: We aimed to determine scope, findability, and quality of MBI-related information on public-facing websites of NCI-designated CCs/CCCs. Methods: We reviewed 64 NCI-designated CC/CCC websites (excluding basic laboratories) between November 2022 and January 2023. We extracted data on the scope of information: (1) type of MBI offered (MT or MS), (2) format (individual, group), (3) method of delivery (in person or remotely delivered), (4) setting (inpatient or outpatient), (5) target population (pediatric or adult), (6) MBI practitioner qualifications, (7) clinical indications or benefits, (8) presence of testimonials, (9) cost, and (10) scheduling or referral information. We also extracted data on findability (ie, presence of direct link or drop-down menu and the number of clicks to locate MBI-related information). Based on the scope and findability data, we rated the information quality as high, moderate, or low using an adapted scale informed by prior research. Results: Thirty-one (48\%) of the 64 CC/CCCs described MBIs on their websites. Of these, 6 (19\%) mentioned both MT and MS, 16 (52\%) mentioned MT only, and 9 (29\%) mentioned MS only. The most common format was hybrid, involving individuals and groups (n=20, 65\%). The most common delivery method was in person (n=16, 52\%). The most common target population was adults (n=12, 39\%). The most common MBI practitioners were board-certified music therapists (n=21, 68\%). The most described indications or benefits were psychological. Twenty-eight (90\%) websites lacked testimonials, and 26 (84\%) lacked cost information. Twenty-six (84\%) websites provided scheduling or referral information. MBI-related information was found with an average of 4 (SD 1) clicks. Nine (29\%) websites were of high quality, 18 (58\%) were moderate, and 4 (13\%) were low. Conclusions: Based on public websites, MBIs were most commonly delivered in person by board-certified music therapists to outpatient and inpatient adults, using individual and group formats to provide psychological benefits. The findability and quality of this information should be improved to promote the dissemination of MBIs for cancer symptom management. ",
doi="10.2196/53440",
url="https://cancer.jmir.org/2024/1/e53440"
}


@Article{info:doi/10.2196/49328,
author="Zhou, Weiqiang
and Liu, Dongliang
and Yi, Zhaoxu
and Lei, Yang
and Zhang, Zhenming
and Deng, Yu
and Tan, Ying",
title="Web-Based Platform for Systematic Reviews and Meta-Analyses of Traditional Chinese Medicine: Platform Development Study",
journal="JMIR Form Res",
year="2024",
month="Nov",
day="22",
volume="8",
pages="e49328",
keywords="evidence-based medicine",
keywords="information science",
keywords="medical librarian",
keywords="web development",
keywords="web design",
keywords="meta-analysis",
keywords="traditional Chinese medicine",
keywords="systematic review",
keywords="review methodology",
keywords="Chinese medicine",
keywords="traditional medicine",
abstract="Background: There are many problems associated with systematic reviews of traditional Chinese medicine (TCM), such as considering ``integrated traditional Chinese and Western medicine'' or treatment methods as intervention measures without considering the differences in drug use, disregarding dosage and courses of treatment, disregarding interindividual differences in control groups, etc. Classifying a large number of heterogeneous intervention measures into the same measure is easy but results in inaccurate results. In April 2023, Cochrane launched RevMan Web to digitalize systematic reviews and meta-analyses. We believe that this web-based working model helps solve the abovementioned problems. Objective: This study aims to (1) develop a web-based platform that is more suitable for systematic review and meta-analysis of TCM and (2) explore the characteristics and future development directions of this work through the testing of digital workflow. Methods: We developed TCMeta (Traditional Chinese Medicine Meta-analysis)---a platform focused on systematic reviews of TCM types. All systematic review--related work can be completed on the web, including creating topics, writing protocols, arranging personnel, obtaining literature, screening literature, inputting and analyzing data, and designing illustrations. The platform was developed using the latest internet technology and can be continuously modified and updated based on user feedback. When screening the literature on the platform, in addition to the traditional manual screening mode, the platform also creatively provides a query mode where users input keywords and click on Search to find literature with the same characteristics; this better reflects the objectivity of the screening with higher efficiency. Productivity can be improved by analyzing data and generating graphs digitally. Results: We used some test data in TCMeta to simulate data processing in a systematic review. In the literature screening stage, researchers could rapidly screen 19 sources of literature from among multiple sources with the manual screening mode. This traditional method could result in bias due to differences in the researchers' cognitive levels. The query mode is much more complex and involves inputting of data regarding drug compatibility, dosage, syndrome type, etc; different query methods can yield very different results, thus increasing the stringency of screening. We integrated data analysis tools on the platform and used third-party software to generate graphs. Conclusions: TCMeta has shown great potential in improving the quality of systematic reviews of TCM types in simulation tests. Several indicators show that this web-based mode of working is superior to the traditional way. Future research is required to focus on validating and refining the performance of TCMeta, emphasizing the ability to handle complex data. The system has good scalability and adaptability, and it has the potential to have a positive impact on the field of evidence-based medicine. ",
doi="10.2196/49328",
url="https://formative.jmir.org/2024/1/e49328"
}


@Article{info:doi/10.2196/60334,
author="Tang, Jian
and Huang, Zikun
and Xu, Hongzhen
and Zhang, Hao
and Huang, Hailing
and Tang, Minqiong
and Luo, Pengsheng
and Qin, Dong",
title="Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation",
journal="JMIR Med Inform",
year="2024",
month="Nov",
day="21",
volume="12",
pages="e60334",
keywords="clinical named entity recognition",
keywords="word embedding",
keywords="Chinese electronic medical records",
keywords="RoBERTa",
keywords="entity recognition",
keywords="segmentation",
keywords="natural language processing",
keywords="AI",
keywords="artificial intelligence",
keywords="dataset",
keywords="dataset augmentation",
keywords="algorithm",
keywords="entity",
keywords="EMR",
abstract="Background: Clinical named entity recognition (CNER) is a fundamental task in natural language processing used to extract named entities from electronic medical record texts. In recent years, with the continuous development of machine learning, deep learning models have replaced traditional machine learning and template-based methods, becoming widely applied in the CNER field. However, due to the complexity of clinical texts, the diversity and large quantity of named entity types, and the unclear boundaries between different entities, existing advanced methods rely to some extent on annotated databases and the scale of embedded dictionaries. Objective: This study aims to address the issues of data scarcity and labeling difficulties in CNER tasks by proposing a dataset augmentation algorithm based on proximity word calculation. Methods: We propose a Segmentation Synonym Sentence Synthesis (SSSS) algorithm based on neighboring vocabulary, which leverages existing public knowledge without the need for manual expansion of specialized domain dictionaries. Through lexical segmentation, the algorithm replaces new synonymous vocabulary by recombining from vast natural language data, achieving nearby expansion expressions of the dataset. We applied the SSSS algorithm to the Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) + conditional random field (CRF) and RoBERTa + Bidirectional Long Short-Term Memory (BiLSTM) + CRF models and evaluated our models (SSSS + RoBERTa + CRF; SSSS + RoBERTa + BiLSTM + CRF) on the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2019 datasets. Results: Our experiments demonstrated that the models SSSS + RoBERTa + CRF and SSSS + RoBERTa + BiLSTM + CRF achieved F1-scores of 91.30\% and 91.35\% on the CCKS-2017 dataset, respectively. They also achieved F1-scores of 83.21\% and 83.01\% on the CCKS-2019 dataset, respectively. Conclusions: The experimental results indicated that our proposed method successfully expanded the dataset and remarkably improved the performance of the model, effectively addressing the challenges of data acquisition, annotation difficulties, and insufficient model generalization performance. ",
doi="10.2196/60334",
url="https://medinform.jmir.org/2024/1/e60334"
}


@Article{info:doi/10.2196/58088,
author="Patel, Mohammed Ahmed
and Baxter, Weston
and Porat, Talya",
title="Toward Guidelines for Designing Holistic Integrated Information Visualizations for Time-Critical Contexts: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="20",
volume="26",
pages="e58088",
keywords="visualization",
keywords="design",
keywords="holistic",
keywords="integrated",
keywords="time-critical",
keywords="guidelines",
keywords="pre-attentive processing",
keywords="gestalt theory",
keywords="situation awareness",
keywords="decision-making",
keywords="mobile phone",
abstract="Background: With the extensive volume of information from various and diverse data sources, it is essential to present information in a way that allows for quick understanding and interpretation. This is particularly crucial in health care, where timely insights into a patient's condition can be lifesaving. Holistic visualizations that integrate multiple data variables into a single visual representation can enhance rapid situational awareness and support informed decision-making. However, despite the existence of numerous guidelines for different types of visualizations, this study reveals that there are currently no specific guidelines or principles for designing holistic integrated information visualizations that enable quick processing and comprehensive understanding of multidimensional data in time-critical contexts. Addressing this gap is essential for enhancing decision-making in time-critical scenarios across various domains, particularly in health care. Objective: This study aims to establish a theoretical foundation supporting the argument that holistic integrated visualizations are a distinct type of visualization for time-critical contexts and identify applicable design principles and guidelines that can be used to design for such cases. Methods: We systematically searched the literature for peer-reviewed research on visualization strategies, guidelines, and taxonomies. The literature selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted across 6 databases: ACM Digital Library, Google Scholar, IEEE Xplore, PubMed, Scopus, and Web of Science. The search was conducted up to August 2024 using the terms (``visualisations'' OR ``visualizations'') AND (``guidelines'' OR ``taxonomy'' OR ``taxonomies''), with studies restricted to the English language. Results: Of 936 papers, 46 (4.9\%) were included in the final review. In total, 48\% (22/46) related to providing a holistic understanding and overview of multidimensional data; 28\% (13/46) focused on integrated presentation, that is, integrating or combining multidimensional data into a single visual representation; and 35\% (16/46) pertained to time and designing for rapid information processing. In total, 65\% (30/46) of the papers presented general information visualization or visual communication guidelines and principles. No specific guidelines or principles were found that addressed all the characteristics of holistic, integrated visualizations in time-critical contexts. A summary of the key guidelines and principles from the 46 papers was extracted, collated, and categorized into 60 guidelines that could aid in designing holistic integrated visualizations. These were grouped according to different characteristics identified in the systematic review (eg, gestalt principles, reduction, organization, abstraction, and task complexity) and further condensed into 5 main proposed guidelines. Conclusions: Holistic integrated information visualizations in time-critical domains are a unique use case requiring a unique set of design guidelines. Our proposed 5 main guidelines, derived from existing design theories and guidelines, can serve as a starting point to enable both holistic and rapid processing of information, facilitating better-informed decisions in time-critical contexts. ",
doi="10.2196/58088",
url="https://www.jmir.org/2024/1/e58088"
}


@Article{info:doi/10.2196/53781,
author="Leblanc, Victor
and Hamroun, Aghiles
and Bentegeac, Rapha{\"e}l
and Le Guellec, Bastien
and Lenain, R{\'e}mi
and Chazard, Emmanuel",
title="Added Value of Medical Subject Headings Terms in Search Strategies of Systematic Reviews: Comparative Study",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="19",
volume="26",
pages="e53781",
keywords="Medical Subject Headings",
keywords="MeSH",
keywords="MeSH thesaurus",
keywords="systematic review",
keywords="PubMed",
keywords="search strategy",
keywords="comparative analysis",
keywords="literature review",
keywords="positive predictive value",
keywords="PPV",
keywords="review",
keywords="scientific knowledge",
keywords="medical knowledge",
keywords="utility",
keywords="systematic literature review",
abstract="Background: The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically. Objective: The objective of this study was to estimate the added value of using MeSH terms in PubMed queries for systematic reviews (SRs). Methods: SRs published in 4 high-impact medical journals in general medicine over the past 10 years were selected. Only SRs for which a PubMed query was provided were included. Each query was transformed to obtain 3 versions: the original query (V1), the query with free-text terms only (V2), and the query with MeSH terms only (V3). These 3 queries were compared with each other based on their sensitivity and positive predictive values. Results: In total, 59 SRs were included. The suppression of MeSH terms had an impact on the number of relevant articles retrieved for 24 (41\%) out of 59 SRs. The median (IQR) sensitivities of queries V1 and V2 were 77.8\% (62.1\%-95.2\%) and 71.4\% (42.6\%-90\%), respectively. V1 queries provided an average of 2.62 additional relevant papers per SR compared with V2 queries. However, an additional 820.29 papers had to be screened. The cost of screening an additional collected paper was therefore 313.09, which was slightly more than triple the mean reading cost associated with V2 queries (88.67). Conclusions: Our results revealed that removing MeSH terms from a query decreases sensitivity while slightly increasing the positive predictive value. Queries containing both MeSH and free-text terms yielded more relevant articles but required screening many additional papers. Despite this additional workload, MeSH terms remain indispensable for SRs. ",
doi="10.2196/53781",
url="https://www.jmir.org/2024/1/e53781"
}


@Article{info:doi/10.2196/58041,
author="Wang, Dingqiao
and Liang, Jiangbo
and Ye, Jinguo
and Li, Jingni
and Li, Jingpeng
and Zhang, Qikai
and Hu, Qiuling
and Pan, Caineng
and Wang, Dongliang
and Liu, Zhong
and Shi, Wen
and Shi, Danli
and Li, Fei
and Qu, Bo
and Zheng, Yingfeng",
title="Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="8",
volume="26",
pages="e58041",
keywords="large language models",
keywords="LLMs",
keywords="retrieval-augmented generation",
keywords="RAG",
keywords="GPT-4.0",
keywords="Claude-2",
keywords="Google Bard",
keywords="diabetes education",
abstract="Background: Large language models (LLMs) demonstrated advanced performance in processing clinical information. However, commercially available LLMs lack specialized medical knowledge and remain susceptible to generating inaccurate information. Given the need for self-management in diabetes, patients commonly seek information online. We introduce the Retrieval-augmented Information System for Enhancement (RISE) framework and evaluate its performance in enhancing LLMs to provide accurate responses to diabetes-related inquiries. Objective: This study aimed to evaluate the potential of the RISE framework, an information retrieval and augmentation tool, to improve the LLM's performance to accurately and safely respond to diabetes-related inquiries. Methods: The RISE, an innovative retrieval augmentation framework, comprises 4 steps: rewriting query, information retrieval, summarization, and execution. Using a set of 43 common diabetes-related questions, we evaluated 3 base LLMs (GPT-4, Anthropic Claude 2, Google Bard) and their RISE-enhanced versions respectively. Assessments were conducted by clinicians for accuracy and comprehensiveness and by patients for understandability. Results: The integration of RISE significantly improved the accuracy and comprehensiveness of responses from all 3 base LLMs. On average, the percentage of accurate responses increased by 12\% (15/129) with RISE. Specifically, the rates of accurate responses increased by 7\% (3/43) for GPT-4, 19\% (8/43) for Claude 2, and 9\% (4/43) for Google Bard. The framework also enhanced response comprehensiveness, with mean scores improving by 0.44 (SD 0.10). Understandability was also enhanced by 0.19 (SD 0.13) on average. Data collection was conducted from September 30, 2023 to February 5, 2024. Conclusions: The RISE significantly improves LLMs' performance in responding to diabetes-related inquiries, enhancing accuracy, comprehensiveness, and understandability. These improvements have crucial implications for RISE's future role in patient education and chronic illness self-management, which contributes to relieving medical resource pressures and raising public awareness of medical knowledge. ",
doi="10.2196/58041",
url="https://www.jmir.org/2024/1/e58041"
}


@Article{info:doi/10.2196/64221,
author="Nachman, Sophie
and Ortiz-Prado, Esteban
and Tucker, D. Joseph",
title="Video Abstracts in Research",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="4",
volume="26",
pages="e64221",
keywords="video abstract",
keywords="abstract",
keywords="dissemination",
keywords="public engagement",
keywords="online",
keywords="videos",
keywords="public audience",
keywords="communication",
keywords="infographics",
keywords="health literacy",
keywords="patient education",
keywords="public health",
doi="10.2196/64221",
url="https://www.jmir.org/2024/1/e64221"
}


@Article{info:doi/10.2196/51095,
author="Zeng, Jiaqi
and Zou, Xiaoyi
and Li, Shirong
and Tang, Yao
and Teng, Sisi
and Li, Huanhuan
and Wang, Changyu
and Wu, Yuxuan
and Zhang, Luyao
and Zhong, Yunheng
and Liu, Jialin
and Liu, Siru",
title="Assessing the Role of the Generative Pretrained Transformer (GPT) in Alzheimer's Disease Management: Comparative Study of Neurologist- and Artificial Intelligence--Generated Responses",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="31",
volume="26",
pages="e51095",
keywords="Alzheimer's disease",
keywords="artificial intelligence",
keywords="AI",
keywords="large language model",
keywords="LLM",
keywords="Generative Pretrained Transformer",
keywords="GPT",
keywords="ChatGPT",
keywords="patient information",
abstract="Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder posing challenges to patients, caregivers, and society. Accessible and accurate information is crucial for effective AD management. Objective: This study aimed to evaluate the accuracy, comprehensibility, clarity, and usefulness of the Generative Pretrained Transformer's (GPT) answers concerning the management and caregiving of patients with AD. Methods: In total, 14 questions related to the prevention, treatment, and care of AD were identified and posed to GPT-3.5 and GPT-4 in Chinese and English, respectively, and 4 respondent neurologists were asked to answer them. We generated 8 sets of responses (total 112) and randomly coded them in answer sheets. Next, 5 evaluator neurologists and 5 family members of patients were asked to rate the 112 responses using separate 5-point Likert scales. We evaluated the quality of the responses using a set of 8 questions rated on a 5-point Likert scale. To gauge comprehensibility and participant satisfaction, we included 3 questions dedicated to each aspect within the same set of 8 questions. Results: As of April 10, 2023, the 5 evaluator neurologists and 5 family members of patients with AD rated the 112 responses: GPT-3.5: n=28, 25\%, responses; GPT-4: n=28, 25\%, responses; respondent neurologists: 56 (50\%) responses. The top 5 (4.5\%) responses rated by evaluator neurologists had 4 (80\%) GPT (GPT-3.5+GPT-4) responses and 1 (20\%) respondent neurologist's response. For the top 5 (4.5\%) responses rated by patients' family members, all but the third response were GPT responses. Based on the evaluation by neurologists, the neurologist-generated responses achieved a mean score of 3.9 (SD 0.7), while the GPT-generated responses scored significantly higher (mean 4.4, SD 0.6; P<.001). Language and model analyses revealed no significant differences in response quality between the GPT-3.5 and GPT-4 models (GPT-3.5: mean 4.3, SD 0.7; GPT-4: mean 4.4, SD 0.5; P=.51). However, English responses outperformed Chinese responses in terms of comprehensibility (Chinese responses: mean 4.1, SD 0.7; English responses: mean 4.6, SD 0.5; P=.005) and participant satisfaction (Chinese responses: mean 4.2, SD 0.8; English responses: mean 4.5, SD 0.5; P=.04). According to the evaluator neurologists' review, Chinese responses had a mean score of 4.4 (SD 0.6), whereas English responses had a mean score of 4.5 (SD 0.5; P=.002). As for the family members of patients with AD, no significant differences were observed between GPT and neurologists, GPT-3.5 and GPT-4, or Chinese and English responses. Conclusions: GPT can provide patient education materials on AD for patients, their families and caregivers, nurses, and neurologists. This capability can contribute to the effective health care management of patients with AD, leading to enhanced patient outcomes. ",
doi="10.2196/51095",
url="https://www.jmir.org/2024/1/e51095"
}


@Article{info:doi/10.2196/60939,
author="Joshi, Saubhagya
and Ha, Eunbin
and Amaya, Andee
and Mendoza, Melissa
and Rivera, Yonaira
and Singh, K. Vivek",
title="Ensuring Accuracy and Equity in Vaccination Information From ChatGPT and CDC: Mixed-Methods Cross-Language Evaluation",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="30",
volume="8",
pages="e60939",
keywords="vaccination",
keywords="health equity",
keywords="multilingualism",
keywords="language equity",
keywords="health literacy",
keywords="online health information",
keywords="conversational agents",
keywords="artificial intelligence",
keywords="large language models",
keywords="health information",
keywords="public health",
abstract="Background: In the digital age, large language models (LLMs) like ChatGPT have emerged as important sources of health care information. Their interactive capabilities offer promise for enhancing health access, particularly for groups facing traditional barriers such as insurance and language constraints. Despite their growing public health use, with millions of medical queries processed weekly, the quality of LLM-provided information remains inconsistent. Previous studies have predominantly assessed ChatGPT's English responses, overlooking the needs of non--English speakers in the United States. This study addresses this gap by evaluating the quality and linguistic parity of vaccination information from ChatGPT and the Centers for Disease Control and Prevention (CDC), emphasizing health equity. Objective: This study aims to assess the quality and language equity of vaccination information provided by ChatGPT and the CDC in English and Spanish. It highlights the critical need for cross-language evaluation to ensure equitable health information access for all linguistic groups. Methods: We conducted a comparative analysis of ChatGPT's and CDC's responses to frequently asked vaccination-related questions in both languages. The evaluation encompassed quantitative and qualitative assessments of accuracy, readability, and understandability. Accuracy was gauged by the perceived level of misinformation; readability, by the Flesch-Kincaid grade level and readability score; and understandability, by items from the National Institutes of Health's Patient?Education?Materials Assessment Tool (PEMAT) instrument. Results: The study found that both ChatGPT and CDC provided mostly accurate and understandable (eg, scores over 95 out of 100) responses. However, Flesch-Kincaid grade levels often exceeded the American Medical Association's recommended levels, particularly in English (eg, average grade level in English for ChatGPT=12.84, Spanish=7.93, recommended=6). CDC responses outperformed ChatGPT in readability across both languages. Notably, some Spanish responses appeared to be direct translations from English, leading to unnatural phrasing. The findings underscore the potential and challenges of using ChatGPT for health care access. Conclusions: ChatGPT holds potential as a health information resource but requires improvements in readability and linguistic equity to be truly effective for diverse populations. Crucially, the default user experience with ChatGPT, typically encountered by those without advanced language and prompting skills, can significantly shape health perceptions. This is vital from a public health standpoint, as the majority of users will interact with LLMs in their most accessible form. Ensuring that default responses are accurate, understandable, and equitable is imperative for fostering informed health decisions across diverse communities. ",
doi="10.2196/60939",
url="https://formative.jmir.org/2024/1/e60939"
}


@Article{info:doi/10.2196/53636,
author="Bardhan, Jayetri
and Roberts, Kirk
and Wang, Zhe Daisy",
title="Question Answering for Electronic Health Records: Scoping Review of Datasets and Models",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="30",
volume="26",
pages="e53636",
keywords="medical question answering",
keywords="electronic health record",
keywords="EHR",
keywords="electronic medical records",
keywords="EMR",
keywords="relational database",
keywords="knowledge graph",
abstract="Background: Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA. Objective: This study aims to provide a methodological review of existing works on QA for EHRs. The objectives of this study were to identify the existing EHR QA datasets and analyze them, study the state-of-the-art methodologies used in this task, compare the different evaluation metrics used by these state-of-the-art models, and finally elicit the various challenges and the ongoing issues in EHR QA. Methods: We searched for articles from January 1, 2005, to September 30, 2023, in 4 digital sources, including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed, to collect relevant publications on EHR QA. Our systematic screening process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 4111 papers were identified for our study, and after screening based on our inclusion criteria, we obtained 47 papers for further study. The selected studies were then classified into 2 non--mutually exclusive categories depending on their scope: ``EHR QA datasets'' and ``EHR QA models.'' Results: A systematic screening process obtained 47 papers on EHR QA for final review. Out of the 47 papers, 53\% (n=25) were about EHR QA datasets, and 79\% (n=37) papers were about EHR QA models. It was observed that QA on EHRs is relatively new and unexplored. Most of the works are fairly recent. In addition, it was observed that emrQA is by far the most popular EHR QA dataset, both in terms of citations and usage in other papers. We have classified the EHR QA datasets based on their modality, and we have inferred that Medical Information Mart for Intensive Care (MIMIC-III) and the National Natural Language Processing Clinical Challenges datasets (ie, n2c2 datasets) are the most popular EHR databases and corpuses used in EHR QA. Furthermore, we identified the different models used in EHR QA along with the evaluation metrics used for these models. Conclusions: EHR QA research faces multiple challenges, such as the limited availability of clinical annotations, concept normalization in EHR QA, and challenges faced in generating realistic EHR QA datasets. There are still many gaps in research that motivate further work. This study will assist future researchers in focusing on areas of EHR QA that have possible future research directions. ",
doi="10.2196/53636",
url="https://www.jmir.org/2024/1/e53636"
}


@Article{info:doi/10.2196/53938,
author="Kim, Minji
and Vassey, Julia
and Li, Dongmei
and Galimov, Artur
and Han, Eileen
and Kirkpatrick, G. Matthew
and Stanton, A. Cassandra
and Ozga, E. Jenny
and Lee, Sarah
and Unger, B. Jennifer",
title="Discussion of Heated Tobacco Products on Twitter Following IQOS's Modified-Risk Tobacco Product Authorization and US Import Ban: Content Analysis",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="24",
volume="26",
pages="e53938",
keywords="heated tobacco products",
keywords="IQOS",
keywords="social media",
keywords="Twitter",
keywords="tobacco control",
keywords="modified-risk tobacco product authorization",
keywords="MRTP authorization",
keywords="tobacco regulatory science",
keywords="import ban",
keywords="observational study",
keywords="public opinion",
keywords="content analysis",
abstract="Background: Understanding public opinions about emerging tobacco products is important to inform future interventions and regulatory decisions. Heated tobacco products (HTPs) are an emerging tobacco product category promoted by the tobacco industry as a ``better alternative'' to combustible cigarettes. Philip Morris International's IQOS is leading the global HTP market and recently has been subject to important policy events, including the US Food and Drug Administration's (FDA) modified-risk tobacco product (MRTP) authorization (July 2020) and the US import ban (November 2021). Although limited in their legal implications outside the United States, these policy events have been quoted in global news outlets and Philip Morris International's promotional communications, showing how they may potentially impact global tobacco regulation. Given the impending return of IQOS to the US market, understanding how the policy events were received through social media discourse will provide valuable insights to inform global tobacco control policy. Objective: This study aims to examine HTP-related social media discourse around important policy events. Methods: We analyzed HTP-related posts on Twitter during the time period that included IQOS's MRTP authorization in the United States and the US import ban, examining personal testimonial, news/information, and direct marketing/retail tweets separately. We also examined how the tweets discussed health and policy. A total of 10,454 public English tweets (posted from June 2020 to December 2021) were collected using HTP-related keywords. We randomly sampled 2796 (26.7\%) tweets and conducted a content analysis. We used pairwise co-occurrence analyses to evaluate connections across themes. Results: Tweet volumes peaked around IQOS-related policy events. Among all tweets, personal testimonials were the most common (1613/2796, 57.7\%), followed by news/information (862/2796, 30.8\%) and direct marketing/retail (321/2796, 11\%). Among personal testimonials, more tweets were positive (495/1613, 30.7\%) than negative (372/1613, 23.1\%), often comparing the health risks of HTPs with cigarettes (402/1613, 24.9\%) or vaping products (252/1613, 15.6\%). Approximately 10\% (31/321) of the direct marketing/retail tweets promoted international delivery, suggesting cross-border promotion. More than a quarter of tweets (809/2796, 28.9\%) discussed US and global policy, including misinterpretation about IQOS being a ``safer'' tobacco product after the US FDA's MRTP authorization. Neutral testimonials mentioning the IQOS brand (634/1613, 39.3\%) and discussing policy (378/1613, 23.4\%) showed the largest pairwise co-occurrence. Conclusions: Results suggest the need for careful communication about the meaning of MRTP authorizations and relative risks of tobacco products. Many tweets expressed HTP-favorable opinions referring to reduced health risks, even though the US FDA has denied marketing of the HTP with reduced risk claims. The popularity of social media as an information source with global reach poses unique challenges in health communication and health policies. While many countries restrict tobacco marketing via the web, our results suggest that retailers may circumvent such regulations by operating overseas. ",
doi="10.2196/53938",
url="https://www.jmir.org/2024/1/e53938",
url="http://www.ncbi.nlm.nih.gov/pubmed/39446431"
}


@Article{info:doi/10.2196/54653,
author="Manion, J. Frank
and Du, Jingcheng
and Wang, Dong
and He, Long
and Lin, Bin
and Wang, Jingqi
and Wang, Siwei
and Eckels, David
and Cervenka, Jan
and Fiduccia, C. Peter
and Cossrow, Nicole
and Yao, Lixia",
title="Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing--Assisted Intelligent Systematic Literature Review System",
journal="JMIR Med Inform",
year="2024",
month="Oct",
day="23",
volume="12",
pages="e54653",
keywords="machine learning",
keywords="deep learning",
keywords="natural language processing",
keywords="systematic literature review",
keywords="artificial intelligence",
keywords="software development",
keywords="data extraction",
keywords="epidemiology",
abstract="Background: Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task. Objective: This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data. Methods: We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning--based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus--associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines. Results: Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks). Conclusions: Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously. ",
doi="10.2196/54653",
url="https://medinform.jmir.org/2024/1/e54653"
}


@Article{info:doi/10.2196/50099,
author="Acero, Nicole
and Herrero, Emma
and Foncham, Juanita
and McIlvaine, Jamie
and Kayaalp, Emre
and Figueora, Melissa
and Oladipo, Francis Antonia",
title="Accuracy, Quality, and Misinformation of YouTube Abortion Procedural Videos: Cross-Sectional Study",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="22",
volume="26",
pages="e50099",
keywords="abortion",
keywords="YouTube",
keywords="social media",
keywords="accuracy",
keywords="quality",
keywords="misinformation",
keywords="reliability",
keywords="obstetrics",
keywords="women's health",
keywords="reproductive",
keywords="patient education",
keywords="health information",
keywords="prochoice",
abstract="Background: The internet is often the first source patients turn to for medical information. YouTube is a commonly used internet-based resource for patients seeking to learn about medical procedures, including their risks, benefits, and safety profile. Abortion is a common yet polarizing medical procedure. People interested in obtaining an abortion are likely to use the internet to learn more about abortion procedures and may encounter misinformed and biased information. This is troubling as information found on the internet can significantly alter perceptions and understanding of these procedures. There is no current research that evaluates the accuracy, quality, and misinformation of instructional abortion videos available to patients. Objective: The purpose of this study was to assess if any given video can deliver accurate and quality information about this topic in an unbiased manner and to assess the level of factually incorrect, distorted, or medically irrelevant information in any given video. Methods: Procedural methods of abortion were queried on YouTube on August 22, 2022. The videos were screened with strict exclusion criteria. Videos were categorized into ``video slants'' based on the language and attitudes expressed in each video. Video accuracy was calculated using the Surgical Curriculum in Obstetrics and Gynecology (SCOG) checklist for each corresponding procedure. Video quality was calculated using the Laparoscopic Surgery Video Educational Guidelines (LAP-VEGaS) criteria. The level of misinformation was assessed with the evidence-based Anti-Choice Rubric, which scores the amount of factually incorrect, distorted, or medically irrelevant information in each video. Results: A total of 32 videos were analyzed and categorized into 3 ``video slant'' groups: neutral (n=23, 72\%), antichoice (n=4, 12\%), and prochoice (n=5, 16\%). Using the SCOG checklist, neutral videos had the highest median accuracy (45.9\%), followed by antichoice videos (24.6\%) and prochoice videos (18.5\%). None of the videos met the LAP-VEGaS quality control criteria, (score>11, indicating adequate quality). Neutral videos had a median score of 8.8 out of 18, with antichoice videos scoring 10.75 and prochoice videos scoring 6.2. Using the Anti-Choice Rubric, neutral videos mentioned only 1 factually incorrect piece of information. Antichoice videos mentioned 12 factually incorrect pieces of information, 8 distortions, and 3 medically irrelevant pieces of information. Prochoice videos did not mention any of the 3 themes. Conclusions: Using the SCOG checklist, the accuracy of instructional videos were inconsistent across the 3 identified ``video slants.'' Using LAP-VEGaS criteria, the quality of educational videos were also inconsistent across the 3 ``video slants.'' Prochoice videos had the lowest level of misinformation, with no mentions of any of the 3 themes. Antichoice videos had the highest levels of misinformation, with mentions in all 3 themes. Health care professionals should consider this when counseling patients who may watch YouTube videos for information regarding abortion procedures. ",
doi="10.2196/50099",
url="https://www.jmir.org/2024/1/e50099"
}


@Article{info:doi/10.2196/60164,
author="Nunes, Miguel
and Bone, Joao
and Ferreira, C. Joao
and Elvas, B. Luis",
title="Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review",
journal="JMIR Med Inform",
year="2024",
month="Oct",
day="21",
volume="12",
pages="e60164",
keywords="language model",
keywords="information extraction",
keywords="healthcare",
keywords="PRISMA-ScR",
keywords="scoping literature review",
keywords="transformers",
keywords="natural language processing",
keywords="European Portuguese",
abstract="Background: In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non--English-language with fewer resources. Objective: This study aimed to research health care IE models, regardless of language, to understand the efficacy of transformers and what are the medical entities most commonly extracted. Methods: This scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) methodology on Scopus and Web of Science Core Collection databases. Only studies that mentioned the creation of health care LMs or health care IE models were included, while large language models (LLMs) were excluded. The latest were not included since we wanted to research LMs and not LLMs, which are architecturally different and have distinct purposes. Results: Our search query retrieved 137 studies, 60 of which met the inclusion criteria, and none of them were systematic literature reviews. English and Chinese are the languages with the most health care LMs developed. These languages already have disease-specific LMs, while others only have general--health care LMs. European Portuguese does not have any public health care LM and should take examples from other languages to develop, first, general-health care LMs and then, in an advanced phase, disease-specific LMs. Regarding IE models, transformers were the most commonly used method, and named entity recognition was the most popular topic, with only a few studies mentioning Assertion Status or addressing medical lexical problems. The most extracted entities were diagnosis, posology, and symptoms. Conclusions: The findings indicate that domain adaptation is beneficial, achieving better results in downstream tasks. Our analysis allowed us to understand that the use of transformers is more developed for the English and Chinese languages. European Portuguese lacks relevant studies and should draw examples from other non-English languages to develop these models and drive progress in AI. Health care professionals could benefit from highlighting medically relevant information and optimizing the reading of the textual data, or this information could be used to create patient medical timelines, allowing for profiling. ",
doi="10.2196/60164",
url="https://medinform.jmir.org/2024/1/e60164",
url="http://www.ncbi.nlm.nih.gov/pubmed/39432345"
}


@Article{info:doi/10.2196/48154,
author="Van Oirschot, Garett
and Pomphrey, Amanda
and Dunne, Caoimhe
and Murphy, Kate
and Blood, Karina
and Doherty, Cailbhe",
title="An Evaluation of the Design of Multimedia Patient Education Materials in Musculoskeletal Health Care: Systematic Review",
journal="JMIR Rehabil Assist Technol",
year="2024",
month="Oct",
day="15",
volume="11",
pages="e48154",
keywords="health education",
keywords="patient education",
keywords="patient education materials",
keywords="multimedia",
keywords="musculoskeletal diseases",
keywords="musculoskeletal pain",
keywords="eHealth",
keywords="self-management",
abstract="Background: Educational multimedia is a cost-effective and straightforward way to administer large-scale information interventions to patient populations in musculoskeletal health care. While an abundance of health research informs the content of these interventions, less guidance exists about optimizing their design. Objective: This study aims to identify randomized controlled trials of patient populations with musculoskeletal conditions that used multimedia-based patient educational materials (PEMs) and examine how design was reported and impacted patients' knowledge and rehabilitation outcomes. Design was evaluated using principles from the cognitive theory of multimedia learning (CTML). Methods: PubMed, CINAHL, PsycINFO, and Embase were searched from inception to September 2023 for studies examining adult patients with musculoskeletal conditions receiving multimedia PEMs compared to any other interventions. The primary outcome was knowledge retention measured via test scores. Secondary outcomes were any patient-reported measures. Retrievability was noted, and PEMs were sourced through search, purchase, and author communication. Results: A total of 160 randomized controlled trials were eligible for inclusion: 13 (8.1\%) included their educational materials and 31 (19.4\%) required a web search, purchase, or direct requests for educational materials. Of these 44 (27.5\%) studies, none fully optimized the design of their educational materials, particularly lacking in the CTML principles of coherence, redundancy, modality, and generative activities for the learner. Of the 160 studies, the remaining 116 (72.5\%) contained interventions that could not be retrieved or appraised. Learning was evaluated in 5 (3.1\%) studies. Conclusions: Musculoskeletal studies should use open science principles and provide their PEMs wherever possible. The link between providing multimedia PEMs and patient learning is largely unexamined, but engagement potential may be maximized when considering design principles such as the CTML. ",
doi="10.2196/48154",
url="https://rehab.jmir.org/2024/1/e48154",
url="http://www.ncbi.nlm.nih.gov/pubmed/39162239"
}


@Article{info:doi/10.2196/60695,
author="Hung, W. Tony K.
and Kuperman, J. Gilad
and Sherman, J. Eric
and Ho, L. Alan
and Weng, Chunhua
and Pfister, G. David
and Mao, J. Jun",
title="Performance of Retrieval-Augmented Large Language Models to Recommend Head and Neck Cancer Clinical Trials",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="15",
volume="26",
pages="e60695",
keywords="large language model",
keywords="LLM",
keywords="ChatGPT",
keywords="GPT-4",
keywords="artificial intelligence",
keywords="AI",
keywords="clinical trials",
keywords="decision support",
keywords="LookUpTrials",
keywords="cancer care delivery",
keywords="head and neck oncology",
keywords="head and neck cancer",
keywords="retrieval augmented generation",
doi="10.2196/60695",
url="https://www.jmir.org/2024/1/e60695"
}


@Article{info:doi/10.2196/56354,
author="Zenone, Marco
and van Schalkwyk, May
and Hartwell, Greg
and Caulfield, Timothy
and Maani, Nason",
title="Selling Misleading ``Cancer Cure'' Books on Amazon: Systematic Search on Amazon.com and Thematic Analysis",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="8",
volume="26",
pages="e56354",
keywords="cancer",
keywords="Amazon",
keywords="misinformation",
keywords="e-commerce",
keywords="cancer cure",
keywords="cancer misinformation",
keywords="misleading",
keywords="cancer information",
keywords="treatment",
keywords="cancer treatment",
keywords="thematic analysis",
keywords="online information",
abstract="Background: While the evidence base on web-based cancer misinformation continues to develop, relatively little is known about the extent of such information on the world's largest e-commerce website, Amazon. Multiple media reports indicate that Amazon may host on its platform questionable cancer-related products for sale, such as books on purported cancer cures. This context suggests an urgent need to evaluate Amazon.com for cancer misinformation. Objective: This study sought to (1) examine to what extent are misleading cancer cure books for sale on Amazon.com and (2) determine how cancer cure books on Amazon.com provide misleading cancer information. Methods: We searched ``cancer cure'' on Amazon.com and retrieved the top 1000 English-language book search results. We reviewed the books' descriptions and titles to determine whether the books provided misleading cancer cure or treatment information. We considered a book to be misleading if it suggested scientifically unsupported cancer treatment approaches to cure or meaningfully treat cancer. Among books coded as misleading, we conducted an inductive latent thematic analysis to determine the informational value the books sought to offer. Results: Nearly half (494/1000, 49.4\%) of the sampled ``cancer cure'' books for sale on Amazon.com appeared to contain misleading cancer treatment and cure information. Overall, 17 (51.5\%) out of 33 Amazon.com results pages had 50\% or more of the books coded as misleading. The first search result page had the highest percentage of misleading books (23/33, 69.7\%). Misleading books (n=494) contained eight themes: (1) claims of efficacious cancer cure strategies (n=451, 91.3\%), (2) oversimplifying cancer and cancer treatment (n=194, 39.3\%), (3) falsely justifying ineffective treatments as science based (n=189, 38.3\%), (4) discrediting conventional cancer treatments (n=169, 34.2\%), (5) finding the true cause of cancer (n=133, 26.9\%), (6) homogenizing cancer (n=132, 26.7\%), (7) discovery of new cancer treatments (n=119, 24.1\%), and (8) cancer cure suppression (n=82, 16.6\%). Conclusions: The results demonstrate that misleading cancer cure books are for sale, visible, and prevalent on Amazon.com, with prominence in initial search hits. These misleading books for sale on Amazon can be conceived of as forming part of a wider, cross-platform, web-based information environment in which misleading cancer cures are often given prominence. Our results suggest that greater enforcement is needed from Amazon and that cancer-focused organizations should engage in preemptive misinformation debunking. ",
doi="10.2196/56354",
url="https://www.jmir.org/2024/1/e56354"
}


@Article{info:doi/10.2196/58831,
author="Armbruster, Jonas
and Bussmann, Florian
and Rothhaas, Catharina
and Titze, Nadine
and Gr{\"u}tzner, Alfred Paul
and Freischmidt, Holger",
title="``Doctor ChatGPT, Can You Help Me?'' The Patient's Perspective: Cross-Sectional Study",
journal="J Med Internet Res",
year="2024",
month="Oct",
day="1",
volume="26",
pages="e58831",
keywords="artificial intelligence",
keywords="AI",
keywords="large language models",
keywords="LLM",
keywords="ChatGPT",
keywords="patient education",
keywords="patient information",
keywords="patient perceptions",
keywords="chatbot",
keywords="chatbots",
keywords="empathy",
abstract="Background: Artificial intelligence and the language models derived from it, such as ChatGPT, offer immense possibilities, particularly in the field of medicine. It is already evident that ChatGPT can provide adequate and, in some cases, expert-level responses to health-related queries and advice for patients. However, it is currently unknown how patients perceive these capabilities, whether they can derive benefit from them, and whether potential risks, such as harmful suggestions, are detected by patients. Objective: This study aims to clarify whether patients can get useful and safe health care advice from an artificial intelligence chatbot assistant. Methods: This cross-sectional study was conducted using 100 publicly available health-related questions from 5 medical specialties (trauma, general surgery, otolaryngology, pediatrics, and internal medicine) from a web-based platform for patients. Responses generated by ChatGPT-4.0 and by an expert panel (EP) of experienced physicians from the aforementioned web-based platform were packed into 10 sets consisting of 10 questions each. The blinded evaluation was carried out by patients regarding empathy and usefulness (assessed through the question: ``Would this answer have helped you?'') on a scale from 1 to 5. As a control, evaluation was also performed by 3 physicians in each respective medical specialty, who were additionally asked about the potential harm of the response and its correctness. Results: In total, 200 sets of questions were submitted by 64 patients (mean 45.7, SD 15.9 years; 29/64, 45.3\% male), resulting in 2000 evaluated answers of ChatGPT and the EP each. ChatGPT scored higher in terms of empathy (4.18 vs 2.7; P<.001) and usefulness (4.04 vs 2.98; P<.001). Subanalysis revealed a small bias in terms of levels of empathy given by women in comparison with men (4.46 vs 4.14; P=.049). Ratings of ChatGPT were high regardless of the participant's age. The same highly significant results were observed in the evaluation of the respective specialist physicians. ChatGPT outperformed significantly in correctness (4.51 vs 3.55; P<.001). Specialists rated the usefulness (3.93 vs 4.59) and correctness (4.62 vs 3.84) significantly lower in potentially harmful responses from ChatGPT (P<.001). This was not the case among patients. Conclusions: The results indicate that ChatGPT is capable of supporting patients in health-related queries better than physicians, at least in terms of written advice through a web-based platform. In this study, ChatGPT's responses had a lower percentage of potentially harmful advice than the web-based EP. However, it is crucial to note that this finding is based on a specific study design and may not generalize to all health care settings. Alarmingly, patients are not able to independently recognize these potential dangers. ",
doi="10.2196/58831",
url="https://www.jmir.org/2024/1/e58831"
}


@Article{info:doi/10.2196/49387,
author="MacNeill, Luke A.
and MacNeill, Lillian
and Luke, Alison
and Doucet, Shelley",
title="Health Professionals' Views on the Use of Conversational Agents for Health Care: Qualitative Descriptive Study",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="25",
volume="26",
pages="e49387",
keywords="conversational agents",
keywords="chatbots",
keywords="health care",
keywords="health professionals",
keywords="health personnel",
keywords="qualitative",
keywords="interview",
abstract="Background: In recent years, there has been an increase in the use of conversational agents for health promotion and service delivery. To date, health professionals' views on the use of this technology have received limited attention in the literature. Objective: The purpose of this study was to gain a better understanding of how health professionals view the use of conversational agents for health care. Methods: Physicians, nurses, and regulated mental health professionals were recruited using various web-based methods. Participants were interviewed individually using the Zoom (Zoom Video Communications, Inc) videoconferencing platform. Interview questions focused on the potential benefits and risks of using conversational agents for health care, as well as the best way to integrate conversational agents into the health care system. Interviews were transcribed verbatim and uploaded to NVivo (version 12; QSR International, Inc) for thematic analysis. Results: A total of 24 health professionals participated in the study (19 women, 5 men; mean age 42.75, SD 10.71 years). Participants said that the use of conversational agents for health care could have certain benefits, such as greater access to care for patients or clients and workload support for health professionals. They also discussed potential drawbacks, such as an added burden on health professionals (eg, program familiarization) and the limited capabilities of these programs. Participants said that conversational agents could be used for routine or basic tasks, such as screening and assessment, providing information and education, and supporting individuals between appointments. They also said that health professionals should have some oversight in terms of the development and implementation of these programs. Conclusions: The results of this study provide insight into health professionals' views on the use of conversational agents for health care, particularly in terms of the benefits and drawbacks of these programs and how they should be integrated into the health care system. These collective findings offer useful information and guidance to stakeholders who have an interest in the development and implementation of this technology. ",
doi="10.2196/49387",
url="https://www.jmir.org/2024/1/e49387",
url="http://www.ncbi.nlm.nih.gov/pubmed/39320936"
}


@Article{info:doi/10.2196/57926,
author="Prakash, Ravi
and Dupre, E. Matthew
and {\O}stbye, Truls
and Xu, Hanzhang",
title="Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study",
journal="JMIR Aging",
year="2024",
month="Sep",
day="24",
volume="7",
pages="e57926",
keywords="electronic health record",
keywords="EHR",
keywords="electric medical record",
keywords="EMR",
keywords="patient record",
keywords="health record",
keywords="personal health record",
keywords="PHR",
keywords="unstructured data",
keywords="rule based analysis",
keywords="artificial intelligence",
keywords="AI",
keywords="large language model",
keywords="LLM",
keywords="natural language processing",
keywords="NLP",
keywords="deep learning",
keywords="Alzheimer's disease and related dementias",
keywords="AD",
keywords="ADRD",
keywords="Alzheimer's disease",
keywords="dementia",
keywords="geriatric syndromes",
abstract="Background: The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or ``hidden'' in unstructured text fields and not readily available for clinicians to act upon. Objective: We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. Methods: We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians' notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, ``mild dementia'' and ``advanced Alzheimer disease''). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. Results: We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93\% (2090/9115) of patients were documented with mild ADRD, 20.87\% (1902/9115) were documented with moderate or severe ADRD, and 56.20\% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95\%, specificity of >95\%, sensitivity of >90\%, and an F1-score of >83\%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91\%, specificity of >80\%, sensitivity of >88\%, and F1-score of >92\%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. Conclusions: Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems. ",
doi="10.2196/57926",
url="https://aging.jmir.org/2024/1/e57926",
url="http://www.ncbi.nlm.nih.gov/pubmed/39316421"
}


@Article{info:doi/10.2196/55182,
author="Mess, Veronica Elisabeth
and Kramer, Frank
and Krumme, Julia
and Kanelakis, Nico
and Teynor, Alexandra",
title="Use of Creative Frameworks in Health Care to Solve Data and Information Problems: Scoping Review",
journal="JMIR Hum Factors",
year="2024",
month="Sep",
day="13",
volume="11",
pages="e55182",
keywords="creative frameworks",
keywords="data and information problems",
keywords="data collection",
keywords="data processing",
keywords="data provision",
keywords="health care",
keywords="information visualization",
keywords="interdisciplinary teams",
keywords="user-centered design",
keywords="user-centered data design",
keywords="user-centric development",
abstract="Background: Digitization is vital for data management, especially in health care. However, problems still hinder health care stakeholders in their daily work while collecting, processing, and providing health data or information. Data are missing, incorrect, cannot be collected, or information is inadequately presented. These problems can be seen as data or information problems. A proven way to elicit requirements for (software) systems is by using creative frameworks (eg, user-centered design, design thinking, lean UX [user experience], or service design) or creative methods (eg, mind mapping, storyboarding, 6 thinking hats, or interaction room). However, to what extent they are used to solve data or information-related problems in health care is unclear. Objective: The primary objective of this scoping review is to investigate the use of creative frameworks in addressing data and information problems in health care. Methods: Following JBI guidelines and the PRISMA-ScR framework, this paper analyzes selected papers, answering whether creative frameworks addressed health care data or information problems. Focusing on data problems (elicitation or collection, processing) and information problems (provision or visualization), the review examined German and English papers published between 2018 and 2022 using keywords related to ``data,'' ``design,'' and ``user-centered.'' The database SCOPUS was used. Results: Of the 898 query results, only 23 papers described a data or information problem and a creative method to solve it. These were included in the follow-up analysis and divided into different problem categories: data collection (n=7), data processing (n=1), information visualization (n=11), and mixed problems meaning data and information problem present (n=4). The analysis showed that most identified problems fall into the information visualization category. This could indicate that creative frameworks are particularly suitable for solving information or visualization problems and less for other, more abstract areas such as data problems. The results also showed that most researchers applied a creative framework after they knew what specific (data or information) problem they had (n=21). Only a minority chose a creative framework to identify a problem and realize it was a data or information problem (n=2). In response to these findings, the paper discusses the need for a new approach that addresses health care data and information challenges by promoting collaboration, iterative feedback, and user-centered development. Conclusions: Although the potential of creative frameworks is undisputed, applying these in solving data and information problems is a minority. To harness this potential, a suitable method needs to be developed to support health care system stakeholders. This method could be the User-Centered Data Approach. ",
doi="10.2196/55182",
url="https://humanfactors.jmir.org/2024/1/e55182"
}


@Article{info:doi/10.2196/47562,
author="Fari{\v c}, Nu{\vs}a
and Potts, WW Henry
and Heilman, M. James",
title="Quality of Male and Female Medical Content on English-Language Wikipedia: Quantitative Content Analysis",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="12",
volume="26",
pages="e47562",
keywords="Wikipedia",
keywords="wikis",
keywords="writing",
keywords="internet",
keywords="health information",
keywords="sex",
keywords="sex bias",
keywords="consumer health information",
keywords="health communication",
keywords="public education",
keywords="social media",
abstract="Background: Wikipedia is the largest free online encyclopedia and the seventh most visited website worldwide, containing >45,000 freely accessible English-language medical articles accessed nearly 1.6 billion times annually. Concerns have been expressed about the balance of content related to biological sex on Wikipedia. Objective: This study aims to categorize the top 1000 most-read (most popular) English-language Wikipedia health articles for June 2019 according to the relevance of the article topic to each sex and quality. Methods: In the first step, Wikipedia articles were identified using WikiProject Medicine Popular Pages. These were analyzed on 13 factors, including total views, article quality, and total number of references. In the second step, 2 general medical textbooks were used as comparators to assess whether Wikipedia's spread of articles was typical compared to the general medical coverage. According to the article's content, we proposed criteria with 5 categories: 1=``exclusively female,'' 2=``predominantly female but can also affect male individuals,'' 3=``not sex specific or neutral,'' 4=predominantly male but can affect female individuals,'' and 5=``exclusively male.'' Results: Of the 1000 Wikipedia health articles, 933 (93.3\%) were not sex specific and 67 (6.7\%) were sex specific. There was no statistically significant difference in the number of reads per month between the sex-specific and non--sex-specific articles (P=.29). Coverage of female topics was higher (50/1000, 5\%) than male topics (17/1000, 1.7\%; this difference was also observed for the 2 medical textbooks, in which 90.2\% (2330/2584) of content was not sex specific, female topics accounted for 8.1\% (209/2584), and male topics for accounted for 1.7\% (45/2584; statistically significant difference; Fisher exact test P=.03). Female-category articles were ranked higher on the Wikipedia medical topic importance list (top, high, or mid importance) than male-category articles (borderline statistical significance; Fisher exact test P=.05). Female articles had a higher number of total and unique references; a slightly higher number of page watchers, pictures, and available languages; and lower number of edits than male articles (all were statistically nonsignificant). Conclusions: Across several metrics, a sample of popular Wikipedia health-related articles for both sexes had comparable quality. Wikipedia had a lower number of female articles and a higher number of neutral articles relative to the 2 medical textbooks. These differences were small, but statistically significant. Higher exclusively female coverage, compared to exclusively male coverage, in Wikipedia articles was similar to the 2 medical textbooks and can be explained by inclusion of sections on obstetrics and gynecology. This is unlike the imbalance seen among biographies of living people, in which approximately 77.6\% pertain to male individuals. Although this study included a small sample of articles, the spread of Wikipedia articles may reflect the readership and the population's content consumption at a given time. Further study of a larger sample of Wikipedia articles would be valuable. ",
doi="10.2196/47562",
url="https://www.jmir.org/2024/1/e47562"
}


@Article{info:doi/10.2196/58705,
author="Manuilova, Iryna
and Bossenz, Jan
and Weise, Bianka Annemarie
and Boehm, Dominik
and Strantz, Cosima
and Unberath, Philipp
and Reimer, Niklas
and Metzger, Patrick
and Pauli, Thomas
and Werle, D. Silke
and Schulze, Susann
and Hiemer, Sonja
and Ustjanzew, Arsenij
and Kestler, A. Hans
and Busch, Hauke
and Brors, Benedikt
and Christoph, Jan",
title="Identifications of Similarity Metrics for Patients With Cancer: Protocol for a Scoping Review",
journal="JMIR Res Protoc",
year="2024",
month="Sep",
day="4",
volume="13",
pages="e58705",
keywords="patient similarity",
keywords="cancer research",
keywords="patient similarity applications",
keywords="precision medicine",
keywords="cancer similarity metrics",
keywords="scoping review protocol",
abstract="Background: Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient. Objective: The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care. Methods: To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research. Results: This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template. Conclusions: The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives. International Registered Report Identifier (IRRID): DERR1-10.2196/58705 ",
doi="10.2196/58705",
url="https://www.researchprotocols.org/2024/1/e58705",
url="http://www.ncbi.nlm.nih.gov/pubmed/39230952"
}


@Article{info:doi/10.2196/55352,
author="Marshall, Zack
and Bhattacharjee, Maushumi
and Wang, Meng
and Cadri, Abdul
and James, Hannah
and Asghari, Shabnam
and Peltekian, Rene
and Benz, Veronica
and Finley-Roy, Vanessa
and Childs, Brynna
and Asaad, Lauren
and Swab, Michelle
and Welch, Vivian
and Brunger, Fern
and Kaposy, Chris",
title="Finding Medical Photographs of Patients Online: Randomized, Cross-Sectional Study",
journal="J Med Internet Res",
year="2024",
month="Jun",
day="24",
volume="26",
pages="e55352",
keywords="patient photographs",
keywords="privacy",
keywords="informed consent",
keywords="publication ethics",
keywords="case reports",
abstract="Background: Photographs from medical case reports published in academic journals have previously been found in online image search results. This means that patient photographs circulate beyond the original journal website and can be freely accessed online. While this raises ethical and legal concerns, no systematic study has documented how often this occurs. Objective: The aim of this cross-sectional study was to provide systematic evidence that patient photographs from case reports published in medical journals appear in Google Images search results. Research questions included the following: (1) what percentage of patient medical photographs published in case reports were found in Google Images search results? (2) what was the relationship between open access publication status and image availability? and (3) did the odds of finding patient photographs on third-party websites differ between searches conducted in 2020 and 2022? Methods: The main outcome measure assessed whether at least 1 photograph from each case report was found on Google Images when using a structured search. Secondary outcome variables included the image source and the availability of images on third-party websites over time. The characteristics of medical images were described using summary statistics. The association between the source of full-text availability and image availability on Google Images was tested using logistic regressions. Finally, we examined the trend of finding patient photographs using generalized estimating equations. Results: From a random sample of 585 case reports indexed in PubMed, 186 contained patient photographs, for a total of 598 distinct images. For 142 (76.3\%) out of 186 case reports, at least 1 photograph was found in Google Images search results. A total of 18.3\% (110/598) of photographs included eye, face, or full body, including 10.9\% (65/598) that could potentially identify the patient. The odds of finding an image from the case report online were higher if the full-text paper was available on ResearchGate (odds ratio [OR] 9.16, 95\% CI 2.71-31.02), PubMed Central (OR 7.90, 95\% CI 2.33-26.77), or Google Scholar (OR 6.07, 95\% CI 2.77-13.29) than if the full-text was available solely through an open access journal (OR 5.33, 95\% CI 2.31-12.28). However, all factors contributed to an increased risk of locating patient images online. Compared with the search in 2020, patient photographs were less likely to be found on third-party websites based on the 2022 search results (OR 0.61, 95\% Cl 0.43-0.87). Conclusions: A high proportion of medical photographs from case reports was found on Google Images, raising ethical concerns with policy and practice implications. Journal publishers and corporations such as Google are best positioned to develop an effective remedy. Until then, it is crucial that patients are adequately informed about the potential risks and benefits of providing consent for clinicians to publish their images in medical journals. ",
doi="10.2196/55352",
url="https://www.jmir.org/2024/1/e55352",
url="http://www.ncbi.nlm.nih.gov/pubmed/38913416"
}


@Article{info:doi/10.2196/52655,
author="Invernici, Francesco
and Bernasconi, Anna
and Ceri, Stefano",
title="Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation",
journal="J Med Internet Res",
year="2024",
month="May",
day="30",
volume="26",
pages="e52655",
keywords="big data corpus",
keywords="clinical research",
keywords="co-occurrence network",
keywords="COVID-19 Open Research Dataset",
keywords="CORD-19",
keywords="graph search",
keywords="Named Entity Recognition",
keywords="Neo4j",
keywords="text mining",
abstract="Background: Since the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications. Objective: This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19--related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. Methods: We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. Results: We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. Conclusions: Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available. ",
doi="10.2196/52655",
url="https://www.jmir.org/2024/1/e52655",
url="http://www.ncbi.nlm.nih.gov/pubmed/38814687"
}


@Article{info:doi/10.2196/49928,
author="Romeiser, L. Jamie
and Jusko, Nicole
and Williams, A. Augusta",
title="Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis",
journal="J Med Internet Res",
year="2024",
month="May",
day="8",
volume="26",
pages="e49928",
keywords="alpha-gal",
keywords="alpha gal",
keywords="alpha-gal syndrome",
keywords="lone star tick",
keywords="infodemiology",
keywords="time series",
keywords="content analysis",
keywords="Google Trends",
keywords="allergy",
keywords="allergic",
keywords="immune",
keywords="immunology",
keywords="immunological",
keywords="information behavior",
keywords="information behaviour",
keywords="information seeking",
keywords="geographic",
abstract="Background: Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. Objective: The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. Methods: Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. Results: Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78\%. The estimated overall change in average search volume was 627\%. In comparison, the average annual percent change was 9.23\% for lone star tick, 7.34\% for meat allergy, and 2.45\% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years ($\rho$=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. Conclusions: The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome. ",
doi="10.2196/49928",
url="https://www.jmir.org/2024/1/e49928",
url="http://www.ncbi.nlm.nih.gov/pubmed/38717813"
}


@Article{info:doi/10.2196/53646,
author="Glayzer, E. Jennifer
and Bray, C. Bethany
and Kobak, H. William
and Steffen, D. Alana
and Schlaeger, M. Judith",
title="Lack of Diversity in Research on Females with Ehlers-Danlos Syndromes: Recruitment Protocol for a Quantitative Online Survey",
journal="JMIR Res Protoc",
year="2024",
month="May",
day="2",
volume="13",
pages="e53646",
keywords="Ehlers-Danlos syndrome",
keywords="hypermobility",
keywords="social media",
keywords="recruitment",
keywords="Facebook",
keywords="hereditary disease",
keywords="connective tissue disorders",
keywords="racial",
keywords="ethnic",
keywords="diversity",
keywords="challenges",
keywords="strategies",
keywords="strategy",
keywords="online",
keywords="information seeking",
keywords="cross-sectional survey",
keywords="dyspareunia",
keywords="painful sex",
keywords="United States",
abstract="Background: Ehlers-Danlos syndromes (EDS) are a group of connective tissue disorders caused by fragile lax collagen. Current EDS research lacks racial and ethnic diversity. The lack of diversity may be associated with the complexities of conducting a large international study on an underdiagnosed condition and a lack of EDS health care providers who diagnose and conduct research outside of the United States and Europe. Social media may be the key to recruiting a large diverse EDS sample. However, studies that have used social media to recruit have not been able to recruit diverse samples. Objective: This study aims to discuss challenges, strategies, outcomes, and lessons learned from using social media to recruit a large sample of females with EDS. Methods: Recruitment on social media for a cross-sectional survey examining dyspareunia (painful sexual intercourse) in females was examined. Inclusion criteria were (1) older than 18 years of age, (2) assigned female at birth, and (3) diagnosed with EDS. Recruitment took place on Facebook and Twitter (now X), from June 1 to June 25, 2019. Results: A total of 1178 females with EDS were recruited from Facebook (n=1174) and X (n=4). On Facebook, participants were recruited via support groups. A total of 166 EDS support groups were identified, 104 permitted the principal investigator to join, 90 approved posting, and the survey was posted in 54 groups. Among them, 30 of the support groups posted in were globally focused and not tied to any specific country or region, 21 were for people in the United States, and 3 were for people outside of the United States. Recruitment materials were posted on X with the hashtag \#EDS. A total of 1599 people accessed the survey and 1178 people were eligible and consented. The average age of participants was 38.6 (SD 11.7) years. Participants were predominantly White (n=1063, 93\%) and non-Hispanic (n=1046, 92\%). Participants were recruited from 29 countries, with 900 (79\%) from the United States and 124 (11\%) from Great Britain. Conclusions: Our recruitment method was successful at recruiting a large sample. The sample was predominantly White and from North America and Europe. More research needs to be conducted on how to recruit a diverse sample. Areas to investigate may include connecting with more support groups from outside the United States and Europe, researching which platforms are popular in different countries, and translating study materials into different languages. A larger obstacle to recruiting diverse samples may be the lack of health care providers that diagnose EDS outside the United States and Europe, making the pool of potential participants small. There needs to be more health care providers that diagnose and treat EDS in countries that are predominantly made up of people of color as well as research that specifically focuses on these populations. International Registered Report Identifier (IRRID): RR1-10.2196/53646 ",
doi="10.2196/53646",
url="https://www.researchprotocols.org/2024/1/e53646",
url="http://www.ncbi.nlm.nih.gov/pubmed/38696252"
}


@Article{info:doi/10.2196/50518,
author="Park, Daemin
and Kim, Dasom
and Park, Ah-hyun",
title="Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022",
journal="J Med Internet Res",
year="2024",
month="Mar",
day="19",
volume="26",
pages="e50518",
keywords="nurses",
keywords="news",
keywords="South Korea",
keywords="natural language processing",
keywords="NLP",
keywords="network analysis",
keywords="politicization",
abstract="Background: In recent years, Korean society has increasingly recognized the importance of nurses in the context of population aging and infectious disease control. However, nurses still face difficulties with regard to policy activities that are aimed at improving the nursing workforce structure and working environment. Media coverage plays an important role in public awareness of a particular issue and can be an important strategy in policy activities. Objective: This study analyzed data from 18 years of news coverage on nursing-related issues. The focus of this study was to examine the drivers of the social, local, economic, and political agendas that were emphasized in the media by the analysis of main sources and their quotes. This analysis revealed which nursing media agendas were emphasized (eg, social aspects), neglected (eg, policy aspects), and negotiated. Methods: Descriptive analysis, natural language processing, and semantic network analysis were applied to analyze data collected from 2005 to 2022. BigKinds were used for the collection of data, automatic multi-categorization of news, named entity recognition of news sources, and extraction and topic modeling of quotes. The main news sources were identified by conducting a 1-mode network analysis with SNAnalyzer. The main agendas of nursing-related news coverage were examined through the qualitative analysis of major sources' quotes by section. The common and individual interests of the top-ranked sources were analyzed through a 2-mode network analysis using UCINET. Results: In total, 128,339 articles from 54 media outlets on nursing-related issues were analyzed. Descriptive analysis showed that nursing-related news was mainly covered in social (99,868/128,339, 77.82\%) and local (48,056/128,339, 48.56\%) sections, whereas it was rarely covered in economic (9439/128,339, 7.35\%) and political (7301/128,339, 5.69\%) sections. Furthermore, 445 sources that had made the top 20 list at least once by year and section were analyzed. Other than ``nurse,'' the main sources for each section were ``labor union,'' ``local resident,'' ``government,'' and ``Moon Jae-in.'' ``Nursing Bill'' emerged as a common interest among nurses and doctors, although the topic did not garner considerable attention from the Ministry of Health and Welfare. Analyzing quotes showed that nurses were portrayed as heroes, laborers, survivors of abuse, and perpetrators. The economic section focused on employment of youth and women in nursing. In the political section, conflicts between nurses and doctors, which may have caused policy confusion, were highlighted. Policy formulation processes were not adequately reported. Media coverage of the enactment of nursing laws tended to relate to confrontations between political parties. Conclusions: The media plays a crucial role in highlighting various aspects of nursing practice. However, policy formulation processes to solve nursing issues were not adequately reported in South Korea. This study suggests that nurses should secure policy compliance by persuading the public to understand their professional perspectives. ",
doi="10.2196/50518",
url="https://www.jmir.org/2024/1/e50518",
url="http://www.ncbi.nlm.nih.gov/pubmed/38393293"
}


@Article{info:doi/10.2196/42849,
author="Ling, Eunice
and de Pieri, Domenico
and Loh, Evenne
and Scott, M. Karen
and Li, H. Stephen C.
and Medbury, J. Heather",
title="Evaluation of the Accuracy, Credibility, and Readability of Statin-Related Websites: Cross-Sectional Study",
journal="Interact J Med Res",
year="2024",
month="Mar",
day="14",
volume="13",
pages="e42849",
keywords="statins",
keywords="consumer health information",
keywords="readability",
keywords="credibility",
keywords="accuracy",
keywords="digital health, health information seeking",
keywords="cardiovascular",
keywords="mortality",
keywords="management",
keywords="pharmacotherapy",
keywords="risk",
keywords="medication",
abstract="Background: Cardiovascular disease (CVD) represents the greatest burden of mortality worldwide, and statins are the most commonly prescribed drug in its management. A wealth of information pertaining to statins and their side effects is on the internet; however, to date, no assessment of the accuracy, credibility, and readability of this information has been undertaken. Objective: This study aimed to evaluate the quality (accuracy, credibility, and readability) of websites likely to be visited by the general public undertaking a Google search of the side effects and use of statin medications. Methods: Following a Google web search, we reviewed the top 20 consumer-focused websites with statin information. Website accuracy, credibility, and readability were assessed based on website category (commercial, not-for-profit, and media), website rank, and the presence or absence of the Health on the Net Code of Conduct (HONcode) seal. Accuracy and credibility were assessed following the development of checklists (with 20 and 13 items, respectively). Readability was assessed using the Simple Measure of Gobbledegook scores. Results: Overall, the accuracy score was low (mean 14.35 out of 20). While side effects were comprehensively covered by 18 websites, there was little information about statin use in primary and secondary prevention. None of the websites met all criteria on the credibility checklist (mean 7.8 out of 13). The median Simple Measure of Gobbledegook score was 9.65 (IQR 8.825-10.85), with none of the websites meeting the recommended reading grade of 6, even the media websites. A website bearing the HONcode seal did not mean that the website was more comprehensive or readable. Conclusions: The quality of statin-related websites tended to be poor. Although the information contained was accurate, it was not comprehensive and was presented at a reading level that was too difficult for an average reader to fully comprehend. As such, consumers risk being uninformed about this pharmacotherapy. ",
doi="10.2196/42849",
url="https://www.i-jmr.org/2024/1/e42849",
url="http://www.ncbi.nlm.nih.gov/pubmed/38483461"
}


@Article{info:doi/10.2196/53372,
author="Bevens, William
and Davenport, Rebekah
and Neate, Sandra
and Yu, Maggie
and Jelinek, Pia
and Jelinek, Alexander George
and Reece, Jeanette",
title="Web-Based Health Information Seeking by People Living With Multiple Sclerosis: Qualitative Investigation of the Multiple Sclerosis Online Course",
journal="J Med Internet Res",
year="2024",
month="Feb",
day="9",
volume="26",
pages="e53372",
keywords="information-seeking behavior",
keywords="self-management",
keywords="lifestyle",
keywords="digital health",
abstract="Background: Digital technologies have afforded people living with multiple sclerosis (MS) access to telehealth consultations, diagnostic tools, and monitoring. Although health care professionals remain the most trusted source of information, the internet has emerged as a valuable resource for providing MS-related information, particularly during the COVID-19 pandemic. Notably, people living with MS are increasingly seeking educational content for a range of topics related to the self-management of MS; however, web-based information seeking remains largely underevaluated. To address this gap and ensure that web-based health-related information is accessible and engaging, this study used qualitative methods to analyze the reflections from participants of web-based educational programs for people living with MS. Objective: This study aimed to explore the motivations, behaviors, and expectations of web-based health information seeking for people living with MS. Methods: We conducted semistructured interviews for 38 people living with MS 1 month after they completed the novel MS Online Course, which provided information on modifiable lifestyle-related risk factors for people living with MS. Of the 38 participants, 22 (58\%) completed the intervention course and 16 (42\%) completed the standard care course. Inductive thematic analysis was used within a qualitative paradigm, and 2 authors coded each interview separately and arrived at themes with consensus. Results: We identified 2 themes: motivation to learn and MS information on the web. The diagnosis of MS was described as a pivotal moment for precipitating web-based information seeking. People living with MS sought lifestyle-related information to facilitate self-management and increase control of their MS. Although social media sites and MS websites were considered useful for providing both support and information, discretion was needed to critically appraise information. Recognizable institutions were frequently accessed because of their trustworthiness. Conclusions: This study provided novel insights into the motivations of people living with MS for seeking web-based health information. Furthermore, their preferences for the content and format of the web-based information accessed and their experiences and reactions to this information were explored. These findings may guide educators, researchers, and clinicians involved in MS care to optimize the engagement and processing of web-based health information seeking by people living with MS. ",
doi="10.2196/53372",
url="https://www.jmir.org/2024/1/e53372",
url="http://www.ncbi.nlm.nih.gov/pubmed/38335016"
}


@Article{info:doi/10.2196/48599,
author="Moens, Maarten
and Van Doorslaer, Leen
and Billot, Maxime
and Eeckman, Edgard
and Roulaud, Manuel
and Rigoard, Philippe
and Fobelets, Maaike
and Goudman, Lisa",
title="Examining the Type, Quality, and Content of Web-Based Information for People With Chronic Pain Interested in Spinal Cord Stimulation: Social Listening Study",
journal="J Med Internet Res",
year="2024",
month="Jan",
day="30",
volume="26",
pages="e48599",
keywords="online information",
keywords="social listening",
keywords="neuromodulation",
keywords="patient care",
keywords="chronic pain",
keywords="web-based data",
abstract="Background: The increased availability of web-based medical information has encouraged patients with chronic pain to seek health care information from multiple sources, such as consultation with health care providers combined with web-based information. The type and quality of information that is available on the web is very heterogeneous, in terms of content, reliability, and trustworthiness. To date, no studies have evaluated what information is available about neuromodulation on the web for patients with chronic pain. Objective: This study aims to explore the type, quality, and content of web-based information regarding spinal cord stimulation (SCS) for chronic pain that is freely available and targeted at health care consumers. Methods: The social listening tool Awario was used to search Facebook (Meta Platforms, Inc), Twitter (Twitter, Inc), YouTube (Google LLC), Instagram (Meta Platforms, Inc), blogs, and the web for suitable hits with ``pain'' and ``neuromodulation'' as keywords. Quality appraisal of the extracted information was performed using the DISCERN instrument. A thematic analysis through inductive coding was conducted. Results: The initial search identified 2174 entries, of which 630 (28.98\%) entries were eventually withheld, which could be categorized as web pages, including news and blogs (114/630, 18.1\%); Reddit (Reddit, Inc) posts (32/630, 5.1\%); Vimeo (Vimeo, Inc) hits (38/630, 6\%); or YouTube (Google LLC) hits (446/630, 70.8\%). Most posts originated in the United States (519/630, 82.4\%). Regarding the content of information, 66.2\% (383/579) of the entries discussed (fully discussed or partially discussed) how SCS works. In total, 55.6\% (322/579) of the entries did not elaborate on the fact that there may be >1 potential treatment choice and 47.7\% (276/579) did not discuss the influence of SCS on the overall quality of life. The inductive coding revealed 4 main themes. The first theme of pain and the burden of pain (1274/8886, 14.34\% coding references) explained about pain, pain management, individual impact of pain, and patient experiences. The second theme included neuromodulation as a treatment approach (3258/8886, 36.66\% coding references), incorporating the background on neuromodulation, patient-centered care, SCS therapy, and risks. Third, several device-related aspects (1722/8886, 19.38\% coding references) were presented. As a final theme, patient benefits and testimonials of treatment with SCS (2632/8886, 29.62\% coding references) were revealed with subthemes regarding patient benefits, eligibility, and testimonials and expectations. Conclusions: Health care consumers have access to web-based information about SCS, where details about the surgical procedures, the type of material, working mechanisms, risks, patient expectations, testimonials, and the potential benefits of this therapy are discussed. The reliability, trustworthiness, and correctness of web-based sources should be carefully considered before automatically relying on the content. ",
doi="10.2196/48599",
url="https://www.jmir.org/2024/1/e48599",
url="http://www.ncbi.nlm.nih.gov/pubmed/38289645"
}


@Article{info:doi/10.2196/57779,
author="Graham, Scott S.
and Shiva, Jade
and Sharma, Nandini
and Barbour, B. Joshua
and Majdik, P. Zoltan
and Rousseau, F. Justin",
title="Conflicts of Interest Publication Disclosures: Descriptive Study",
journal="JMIR Data",
year="2024",
month="Oct",
day="31",
volume="5",
pages="e57779",
keywords="conflicts of interest",
keywords="biomedical publishing",
keywords="research integrity",
keywords="dataset",
keywords="COI",
keywords="ethical",
keywords="ethics",
keywords="publishing",
keywords="drugs",
keywords="pharmacies",
keywords="pharmacology",
keywords="pharmacotherapy",
keywords="pharmaceuticals",
keywords="medication",
keywords="disclosure",
keywords="information science",
keywords="library science",
keywords="open data",
abstract="Background: Multiple lines of previous research have documented that author conflicts of interest (COI) can compromise the integrity of the biomedical research enterprise. However, continuing research that would investigate why, how, and in what circumstances COI is most risky is stymied by the difficulty in accessing disclosure statements, which are not widely represented in available databases. Objective: In this study, we describe a new open access dataset of COI disclosures extracted from published biomedical journal papers. Methods: To develop the dataset, we used ClinCalc's Top 300 drugs lists for 2017 and 2018 to identify 319 of the most commonly used drugs. Search strategies for each product were developed using the National Library of Medicine's and MeSH (Medical Subject Headings) browser and deployed using the eUtilities application programming interface in April 2021. We identified the 150 most relevant papers for each product and extracted COI disclosure statements from PubMed, PubMed Central, or retrieved papers as necessary. Results: Conflicts of Interest Publication Disclosures (COIPonD) is a new dataset that captures author-reported COI disclosures for biomedical research papers published in a wide range of journals and subspecialties. COIPonD captures author-reported disclosure information (including lack of disclosure) for over 38,000 PubMed-indexed papers published between 1949 and 2022. The collected papers are indexed by discussed drug products with a focus on the 319 most commonly used drugs in the United States. Conclusions: COIPonD should accelerate research efforts to understand the effects of COI on the biomedical research enterprise. In particular, this dataset should facilitate new studies of COI effects across disciplines and subspecialties. ",
doi="10.2196/57779",
url="https://data.jmir.org/2024/1/e57779"
}


@Article{info:doi/10.2196/42619,
author="Taira, Kazuya
and Itaya, Takahiro
and Fujita, Sumio",
title="Predicting Smoking Prevalence in Japan Using Search Volumes in an Internet Search Engine: Infodemiology Study",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="14",
volume="24",
number="12",
pages="e42619",
keywords="health policy",
keywords="internet use",
keywords="quality indicators",
keywords="search engine",
keywords="smoking",
keywords="tobacco use",
keywords="public health",
keywords="infodemiology",
keywords="smoking trend",
keywords="health indicator",
keywords="health promotion",
abstract="Background: Tobacco smoking is an important public health issue and a core indicator of public health policy worldwide. However, global pandemics and natural disasters have prevented surveys from being conducted. Objective: The purpose of this study was to predict smoking prevalence by prefecture and sex in Japan using Internet search trends. Methods: This study used the infodemiology approach. The outcome variable was smoking prevalence by prefecture, obtained from national surveys. The predictor variables were the search volumes on Yahoo! Japan Search. We collected the search volumes for queries related to terms from the thesaurus of the Japanese medical article database Ichu-shi. Predictor variables were converted to per capita values and standardized as z scores. For smoking prevalence, the values for 2016 and 2019 were used, and for search volume, the values for the April 1 to March 31 fiscal year (FY) 1 year prior to the survey (ie, FY 2015 and FY 2018) were used. Partial correlation coefficients, adjusted for data year, were calculated between smoking prevalence and search volume, and a regression analysis using a generalized linear mixed model with random effects was conducted for each prefecture. Several models were tested, including a model that included all search queries, a variable reduction method, and one that excluded cigarette product names. The best model was selected with the Akaike information criterion corrected (AICC) for small sample size and the Bayesian information criterion (BIC). We compared the predicted and actual smoking prevalence in 2016 and 2019 based on the best model and predicted the smoking prevalence in 2022. Results: The partial correlation coefficients for men showed that 9 search queries had significant correlations with smoking prevalence, including cigarette (r=--0.417, P<.001), cigar in kanji (r=--0.412, P<.001), and cigar in katakana (r=-0.399, P<.001). For women, five search queries had significant correlations, including vape (r=0.335, P=.001), quitting smoking (r=0.288, P=.005), and cigar (r=0.286, P=.006). The models with all search queries were the best models for both AICC and BIC scores. Scatter plots of actual and estimated smoking prevalence in 2016 and 2019 confirmed a relatively high degree of agreement. The average estimated smoking prevalence in 2022 in the 47 prefectures for the total sample was 23.492\% (95\% CI 21.617\%-25.367\%), showing an increasing trend, with an average of 29.024\% (95\% CI 27.218\%-30.830\%) for men and 8.793\% (95\% CI 7.531\%-10.054\%) for women. Conclusions: This study suggests that the search volume of tobacco-related queries in internet search engines can predict smoking prevalence by prefecture and sex in Japan. These findings will enable the development of low-cost, timely, and crisis-resistant health indicators that will enable the evaluation of health measures and contribute to improved public health. ",
doi="10.2196/42619",
url="https://www.jmir.org/2022/12/e42619",
url="http://www.ncbi.nlm.nih.gov/pubmed/36515993"
}


@Article{info:doi/10.2196/41219,
author="?ulea, M. Cristina
and N?d??an, Valentin
and Ursachi, Tatiana
and Toboltoc, Paul-C?t?lin
and Benedek, Theodora",
title="What Patients Find on the Internet When Looking for Information About Percutaneous Coronary Intervention: Multilanguage Cross-sectional Assessment",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="6",
volume="24",
number="12",
pages="e41219",
keywords="percutaneous coronary intervention",
keywords="consumer health informatics",
keywords="internet",
keywords="health education",
keywords="health information",
keywords="quality",
keywords="reliability",
keywords="informed decision-making",
keywords="credibility",
keywords="content quality",
keywords="medical information",
abstract="Background: The internet provides general users with wide access to medical information. However, regulating and controlling the quality and reliability of the considerable volume of available data is challenging, thus generating concerns about the consequences of inaccurate health care--related documentation. Several tools have been proposed to increase the transparency and overall trustworthiness of medical information present on the web. Objective: We aimed to analyze and compare the quality and reliability of information about percutaneous coronary intervention on English, German, Hungarian, Romanian, and Russian language websites. Methods: Following a rigorous protocol, 125 websites were selected, 25 for each language sub-sample. The websites were assessed concerning their general characteristics, compliance with a set of eEurope 2002 credibility criteria, and quality of the informational content (namely completeness and accuracy), based on a topic-specific benchmark. Completeness and accuracy were graded independently by 2 evaluators. Scores were reported on a scale from 0 to 10. The 5 language subsamples were compared regarding credibility, completeness, and accuracy. Correlations between credibility scores on the one hand, and completeness and accuracy scores, on the other hand, were tested within each language subsample. Results: The websites' compliance with credibility criteria was average at best with scores between 3.0 and 6.0. In terms of completeness and accuracy, the website subsets qualified as poor or average, with scores ranging from 2.4 to 4.6 and 3.6 to 5.3, respectively. English language websites scored significantly higher in all 3 aspects, followed by German and Hungarian language websites. Only German language websites showed a significant correlation between credibility and information quality. Conclusions: The quality of websites in English, German, Hungarian, Romanian, and Russian languages about percutaneous coronary intervention was rather inadequate and may raise concerns regarding their impact on informed decision-making. Using credibility criteria as indicators of information quality may not be warranted, as credibility scores were only exceptionally correlated with content quality. The study brings valuable descriptive data on the quality of web-based information regarding percutaneous coronary intervention in multiple languages and raises awareness about the need for responsible use of health-related web resources. ",
doi="10.2196/41219",
url="https://www.jmir.org/2022/12/e41219",
url="http://www.ncbi.nlm.nih.gov/pubmed/36472906"
}


@Article{info:doi/10.2196/41527,
author="Patton, Thomas
and Abramovitz, Daniela
and Johnson, Derek
and Leas, Eric
and Nobles, Alicia
and Caputi, Theodore
and Ayers, John
and Strathdee, Steffanie
and B{\'o}rquez, Annick",
title="Characterizing Help-Seeking Searches for Substance Use Treatment From Google Trends and Assessing Their Use for Infoveillance: Longitudinal Descriptive and Validation Statistical Analysis",
journal="J Med Internet Res",
year="2022",
month="Dec",
day="1",
volume="24",
number="12",
pages="e41527",
keywords="internet",
keywords="search",
keywords="help-seeking",
keywords="substance use treatment",
keywords="surveillance",
keywords="infoveillance",
keywords="google trends",
abstract="Background: There is no recognized gold standard method for estimating the number of individuals with substance use disorders (SUDs) seeking help within a given geographical area. This presents a challenge to policy makers in the effective deployment of resources for the treatment of SUDs. Internet search queries related to help seeking for SUDs using Google Trends may represent a low-cost, real-time, and data-driven infoveillance tool to address this shortfall in information. Objective: This paper assesses the feasibility of using search query data related to help seeking for SUDs as an indicator of unmet treatment needs, demand for treatment, and predictor of the health harms related to unmet treatment needs. We explore a continuum of hypotheses to account for different outcomes that might be expected to occur depending on the demand for treatment relative to the system capacity and the timing of help seeking in relation to trajectories of substance use and behavior change. Methods: We used negative binomial regression models to examine temporal trends in the annual SUD help-seeking internet search queries from Google Trends by US state for cocaine, methamphetamine, opioids, cannabis, and alcohol from 2010 to 2020. To validate the value of these data for surveillance purposes, we then used negative binomial regression models to investigate the relationship between SUD help-seeking searches and state-level outcomes across the continuum of care (including lack of care). We started by looking at associations with self-reported treatment need using data from the National Survey on Drug Use and Health, a national survey of the US general population. Next, we explored associations with treatment admission rates from the Treatment Episode Data Set, a national data system on SUD treatment facilities. Finally, we studied associations with state-level rates of people experiencing and dying from an opioid overdose, using data from the Agency for Healthcare Research and Quality and the CDC WONDER database. Results: Statistically significant differences in help-seeking searches were observed over time between 2010 and 2020 (based on P<.05 for the corresponding Wald tests). We were able to identify outlier states for each drug over time (eg, West Virginia for both opioids and methamphetamine), indicating significantly higher help-seeking behaviors compared to national trends. Results from our validation analyses across different outcomes showed positive, statistically significant associations for the models relating to treatment need for alcohol use, treatment admissions for opioid and methamphetamine use, emergency department visits related to opioid use, and opioid overdose mortality data (based on regression coefficients having P?.05). Conclusions: This study demonstrates the clear potential for using internet search queries from Google Trends as an infoveillance tool to predict the demand for substance use treatment spatially and temporally, especially for opioid use disorders. ",
doi="10.2196/41527",
url="https://www.jmir.org/2022/12/e41527",
url="http://www.ncbi.nlm.nih.gov/pubmed/36454620"
}


@Article{info:doi/10.2196/38425,
author="Ackleh-Tingle, V. Jonathan
and Jordan, M. Natalie
and Onwubiko, N. Udodirim
and Chandra, Christina
and Harton, E. Paige
and Rentmeester, T. Shelby
and Chamberlain, T. Allison",
title="Prevalence and Correlates of COVID-19 Vaccine Information on Family Medicine Practices' Websites in the United States: Cross-sectional Website Content Analysis",
journal="JMIR Form Res",
year="2022",
month="Nov",
day="17",
volume="6",
number="11",
pages="e38425",
keywords="primary care",
keywords="vaccine hesitancy",
keywords="COVID-19",
keywords="health communications",
keywords="health information",
keywords="health website",
keywords="family practice",
keywords="vaccine information",
keywords="online health",
keywords="health platform",
keywords="online information",
abstract="Background: Primary care providers are regarded as trustworthy sources of information about COVID-19 vaccines. Although primary care practices often provide information about common medical and public health topics on their practice websites, little is known about whether they also provide information about COVID-19 vaccines on their practice websites. Objective: This study aimed to investigate the prevalence and correlates of COVID-19 vaccine information on family medicine practices' website home pages in the United States. Methods: We used the Centers for Medicare and Medicaid National Provider Identifier records to create a sampling frame of all family medicine providers based in the United States, from which we constructed a nationally representative random sample of 964 family medicine providers. Between September 20 and October 8, 2021, we manually examined the practice websites of these providers and extracted data on the availability of COVID-19 vaccine information, and we implemented a 10\% cross-review quality control measure to resolve discordances in data abstraction. We estimated the prevalence of COVID-19 vaccine information on practice websites and website home pages and used Poisson regression with robust error variances to estimate crude and adjusted prevalence ratios for correlates of COVID-19 vaccine information, including practice size, practice region, university affiliation, and presence of information about seasonal influenza vaccines. Additionally, we performed sensitivity analyses to account for multiple comparisons. Results: Of the 964 included family medicine practices, most (n=509, 52.8\%) had ?10 distinct locations, were unaffiliated with a university (n=838, 87.2\%), and mentioned seasonal influenza vaccines on their websites (n=540, 56.1\%). In total, 550 (57.1\%) practices mentioned COVID-19 vaccines on their practices' website home page, specifically, and 726 (75.3\%) mentioned COVID-19 vaccines anywhere on their practice website. As practice size increased, the likelihood of finding COVID-19 vaccine information on the home page increased (n=66, 27.7\% among single-location practices, n=114, 52.5\% among practices with 2-9 locations, n=66, 56.4\% among practices with 10-19 locations, and n=304, 77.6\% among practices with 20 or more locations, P<.001 for trend). Compared to clinics in the Northeast, those in the West and Midwest United States had a similar prevalence of COVID-19 vaccine information on website home pages, but clinics in the south had a lower prevalence (adjusted prevalence ratio 0.8, 95\% CI 0.7 to 1.0; P=.02). Our results were largely unchanged in sensitivity analyses accounting for multiple comparisons. Conclusions: Given the ongoing COVID-19 pandemic, primary care practitioners who promote and provide vaccines should strongly consider utilizing their existing practice websites to share COVID-19 vaccine information. These existing platforms have the potential to serve as an extension of providers' influence on established and prospective patients who search the internet for information about COVID-19 vaccines. ",
doi="10.2196/38425",
url="https://formative.jmir.org/2022/11/e38425",
url="http://www.ncbi.nlm.nih.gov/pubmed/36343211"
}


@Article{info:doi/10.2196/42447,
author="Zhao, Chris Yuxiang
and Zhao, Mengyuan
and Song, Shijie",
title="Online Health Information Seeking Among Patients With Chronic Conditions: Integrating the Health Belief Model and Social Support Theory",
journal="J Med Internet Res",
year="2022",
month="Nov",
day="2",
volume="24",
number="11",
pages="e42447",
keywords="health information seeking",
keywords="patients with chronic conditions",
keywords="health belief model, social support",
keywords="critical health literacy",
abstract="Background: Chronic diseases are the leading causes of death and disability. With the growing patient population and climbing health care expenditures, researchers and policy makers are seeking new approaches to improve the accessibility of health information on chronic diseases while lowering costs. Online health information sources can play a substantial role in effective patient education and health communication. However, some contradictory evidence suggests that patients with chronic conditions may not necessarily seek online health information. Objective: This study aims to integrate 2 theories (ie, the health belief model and social support theory) and a critical health literacy perspective to understand online health information seeking (OHIS) among patients with chronic conditions. Methods: We used the survey method to collect data from online chronic disease communities and groups on social media platforms. Eligible participants were consumers with at least 1 chronic condition and those who have experience with OHIS. A total of 390 valid questionnaires were collected. The partial least squares approach to structural equation modeling was employed to analyze the data. Results: The results suggested that perceived risk (t=3.989, P<.001) and perceived benefits (t=3.632, P<.001) significantly affected patients' OHIS. Perceived susceptibility (t=7.743, P<.001) and perceived severity (t=8.852, P<.001) were found to influence the perceived risk of chronic diseases significantly. Informational support (t=5.761, P<.001) and emotional support (t=5.748, P<.001) also impacted the perceived benefits of online sources for patients. In addition, moderation analysis showed that critical health literacy significantly moderated the link between perceived risk and OHIS (t=3.097, P=.002) but not the relationship between perceived benefits and OHIS (t=0.288, P=.774). Conclusions: This study shows that the health belief model, when combined with social support theory, can predict patients' OHIS. The perceived susceptibility and severity can effectively explain perceived risk, further predicting patients' OHIS. Informational support and emotional support can contribute to perceived benefits, thereby positively affecting patients' OHIS. This study also demonstrated the important negative moderating effects of critical health literacy on the association between perceived risk and OHIS. ",
doi="10.2196/42447",
url="https://www.jmir.org/2022/11/e42447",
url="http://www.ncbi.nlm.nih.gov/pubmed/36322124"
}


@Article{info:doi/10.2196/39946,
author="Lin, Michelle
and Phipps, Mina
and Yilmaz, Yusuf
and Nash, J. Christopher
and Gisondi, A. Michael
and Chan, M. Teresa",
title="A Fork in the Road for Emergency Medicine and Critical Care Blogs and Podcasts: Cross-sectional Study",
journal="JMIR Med Educ",
year="2022",
month="Oct",
day="28",
volume="8",
number="4",
pages="e39946",
keywords="open educational resource",
keywords="free open-access meducation",
keywords="FOAM",
keywords="meducation",
keywords="open-access",
keywords="internet based",
keywords="web based",
keywords="website",
keywords="social media",
keywords="medical education",
keywords="disruptive innovation",
keywords="blog",
keywords="podcast",
keywords="emergency",
keywords="critical care",
abstract="Background: Free open-access meducation (FOAM) refers to open-access, web-based learning resources in medicine. It includes all formats of digital products, including blogs and podcasts. The number of FOAM blog and podcast sites in emergency medicine and critical care increased dramatically from 2002 to 2013, and physicians began to rely on the availability of these resources. The current landscape of these FOAM sites is unknown. Objective: This study aims to (1) estimate the current number of active, open-access blogs and podcasts in emergency medicine and critical care and (2) describe observed and anticipated trends in the FOAM movement using the Theory of Disruptive Innovation by Christensen as a theoretical framework. Methods: The authors used multiple resources and sampling strategies to identify active, open-access blogs and podcasts between April 25, 2022, and May 8, 2022, and classified these websites as blogs, podcasts, or blogs+podcasts. For each category, they reported the following outcome measures using descriptive statistics: age, funding, affiliations, and team composition. Based on these findings, the authors projected trends in the number of active sites using a positivist paradigm and the Theory of Disruptive Innovation as a theoretical framework. Results: The authors identified 109 emergency medicine and critical care websites, which comprised 45.9\% (n=50) blogs, 22.9\% (n=25) podcasts, and 31.2\% (n=34) blogs+podcasts. Ages ranged from 0 to 18 years; 27.5\% (n=30) sold products, 18.3\% (n=20) used advertisements, 44.0\% (n=48) had institutional funding, and 27.5\% (n=30) had no affiliation or external funding sources. Team sizes ranged from 1 (n=26, 23.9\%) to ?5 (n=60, 55\%) individuals. Conclusions: There was a sharp decline in the number of emergency medicine and critical care blogs and podcasts in the last decade, dropping 40.4\% since 2013. The initial growth of FOAM and its subsequent downturn align with principles in the Theory of Disruptive Innovation by Christensen. These findings have important implications for the field of medical education. ",
doi="10.2196/39946",
url="https://mededu.jmir.org/2022/4/e39946",
url="http://www.ncbi.nlm.nih.gov/pubmed/36306167"
}


@Article{info:doi/10.2196/37845,
author="van Deursen, M. Alexander J. A.",
title="General Health Statuses as Indicators of Digital Inequality and the Moderating Effects of Age and Education: Cross-sectional Study",
journal="J Med Internet Res",
year="2022",
month="Oct",
day="21",
volume="24",
number="10",
pages="e37845",
keywords="digital inequality",
keywords="health",
keywords="MOS",
keywords="eHealth",
keywords="digital health",
keywords="online health",
keywords="age",
keywords="education",
keywords="survey",
keywords="digital divide",
keywords="attitude",
keywords="health outcome",
keywords="patient outcome",
keywords="internet access",
keywords="internet skill",
keywords="technology skill",
abstract="Background: Considerable effort has been directed to offering online health information and services aimed at the general population. Such efforts potentially support people to obtain improved health outcomes. However, when health information and services are moved online, issues of equality need to be considered. In this study, we focus on the general population and take as a point of departure how health statuses (physical functioning, social functioning, mental health, perceived health, and physical pain) are linked to internet access (spanning internet attitude, material access, internet skills, and health-related internet use). Objective: This study aims to reveal to what extent (1) internet access is important for online health outcomes, (2) different health statuses are important for obtaining internet access and outcomes, and (3) age and education moderate the contribution of health statuses to internet access. Methods: A sequence of 2 online surveys drawing upon a sample collected in the Netherlands was used, and a data set with 1730 respondents over the age of 18 years was obtained. Results: Internet attitude contributes positively to material access, internet skills, and health outcomes and negatively to health-related internet use. Material access contributes positively to internet skills and health-related internet use and outcomes. Internet skills contribute positively to health-related internet use and outcomes. Physical functioning contributes positively to internet attitude, material access, and internet skills but negatively to internet health use. Social functioning contributes negatively to internet attitude and positively to internet skills and internet health use. Mental health contributes positively to internet attitude and negatively to material access and internet health use. Perceived health positively contributes to material access, internet skills, and internet health use. Physical pain contributes positively to internet attitude and material access and indirectly to internet skills and internet health use. Finally, most contributions are moderated by age (<65 and ?65 years) and education (low and high). Conclusions: To make online health care attainable for the general population, interventions should focus simultaneously on internet attitude, material access, internet skills, and internet health use. However, issues of equality need to be considered. In this respect, digital inequality research benefits from considering health as a predictor of all 4 access stages. Furthermore, studies should go beyond single self-reported measures of health. Physical functioning, social functioning, mental health, perceived health, and physical pain all show unique contributions to the different internet access stages. Further complicating this issue, online health-related interventions for people with different health statuses should also consider age and the educational level of attainment. ",
doi="10.2196/37845",
url="https://www.jmir.org/2022/10/e37845",
url="http://www.ncbi.nlm.nih.gov/pubmed/36269664"
}


@Article{info:doi/10.2196/39555,
author="Alexander, Shelley
and Seenan, Chris",
title="Credibility, Accuracy, and Comprehensiveness of Readily Available Internet-Based Information on Treatment and Management of Peripheral Artery Disease and Intermittent Claudication: Review",
journal="J Med Internet Res",
year="2022",
month="Oct",
day="17",
volume="24",
number="10",
pages="e39555",
keywords="peripheral artery disease",
keywords="intermittent claudication",
keywords="health information",
keywords="education",
keywords="internet",
keywords="eHealth",
keywords="digital health",
abstract="Background: Peripheral artery disease (PAD) affects millions of people worldwide, and a core component of management of the condition is self-management. The internet is an important source of health information for many people. However, the content of websites regarding treatment recommendations for PAD has not been fully evaluated. Objective: This study aimed to assess the credibility, accuracy, and comprehensiveness of websites found via a common search engine, by comparing the content to current guidelines for treatment and management of PAD and intermittent claudication (IC). Methods: A review of websites from hospitals, universities, governments, consumer organizations, and professional associations in the United States and the United Kingdom was conducted. Website recommendations for the treatment of PAD and IC were coded in accordance with the guidelines of the National Institute for Health and Care Excellence (NICE) and the American Heart Association (AHA). Primary outcomes were website credibility (4-item Journal of the American Medical Association benchmark), website accuracy (in terms of the percentage of accurate recommendations), and comprehensiveness of website recommendations (in terms of the percentage of guideline recommendations that were appropriately covered). Secondary outcomes were readability (Flesch--Kincaid grade level) and website quality (Health On the Net Foundation's code of conduct). Results: After screening, 62 websites were included in this analysis. Only 45\% (28/62) of websites met the credibility requirement by stating they were updated after the NICE guidelines were published. Declaration of authorship and funding and the presence of reference lists were less commonly reported. Regarding accuracy, 81\% (556/685) of website recommendations were deemed accurate on following NICE's and the AHA's recommendations. Comprehensiveness was low, with an average of 40\% (25/62) of guideline treatment recommendations being appropriately covered by websites. In most cases, readability scores revealed that the websites were too complex for web-based consumer health information. Conclusions: Web-based information from reputable sources about the treatment and management of PAD and IC are generally accurate but have low comprehensiveness, credibility, and readability. ",
doi="10.2196/39555",
url="https://www.jmir.org/2022/10/e39555",
url="http://www.ncbi.nlm.nih.gov/pubmed/36251363"
}


@Article{info:doi/10.2196/41012,
author="Dub{\'e}, Eve
and MacDonald, E. Shannon
and Manca, Terra
and Bettinger, A. Julie
and Driedger, Michelle S.
and Graham, Janice
and Greyson, Devon
and MacDonald, E. Noni
and Meyer, Samantha
and Roch, Genevi{\`e}ve
and Vivion, Maryline
and Aylsworth, Laura
and Witteman, O. Holly
and G{\'e}linas-Gascon, F{\'e}lix
and Marques Sathler Guimaraes, Lucas
and Hakim, Hina
and Gagnon, Dominique
and B{\'e}chard, Beno{\^i}t
and Gramaccia, A. Julie
and Khoury, Richard
and Tremblay, S{\'e}bastien",
title="Understanding the Influence of Web-Based Information, Misinformation, Disinformation, and Reinformation on COVID-19 Vaccine Acceptance: Protocol for a Multicomponent Study",
journal="JMIR Res Protoc",
year="2022",
month="Oct",
day="17",
volume="11",
number="10",
pages="e41012",
keywords="vaccine hesitancy",
keywords="COVID-19",
keywords="misinformation",
keywords="vaccine decisions",
keywords="disinformation",
keywords="online",
keywords="vaccine",
keywords="vaccination",
abstract="Background: The COVID-19 pandemic has generated an explosion in the amount of information shared on the internet, including false and misleading information on SARS-CoV-2 and recommended protective behaviors. Prior to the pandemic, web-based misinformation and disinformation were already identified as having an impact on people's decision to refuse or delay recommended vaccination for themselves or their children. Objective: The overall aims of our study are to better understand the influence of web-based misinformation and disinformation on COVID-19 vaccine decisions and investigate potential solutions to reduce the impact of web-based misinformation and disinformation about vaccines. Methods: Based on different research approaches, the study will involve (1) the use of artificial intelligence techniques, (2) a web-based survey, (3) interviews, and (4) a scoping review and an environmental scan of the literature. Results: As of September 1, 2022, data collection has been completed for all objectives. The analysis is being conducted, and results should be disseminated in the upcoming months. Conclusions: The findings from this study will help with understanding the underlying determinants of vaccine hesitancy among Canadian individuals and identifying effective, tailored interventions to improve vaccine acceptance among them. International Registered Report Identifier (IRRID): DERR1-10.2196/41012 ",
doi="10.2196/41012",
url="https://www.researchprotocols.org/2022/10/e41012",
url="http://www.ncbi.nlm.nih.gov/pubmed/36191171"
}


@Article{info:doi/10.2196/38641,
author="Jawad, Danielle
and Cheng, Heilok
and Wen, Ming Li
and Rissel, Chris
and Baur, Louise
and Mihrshahi, Seema
and Taki, Sarah",
title="Interactivity, Quality, and Content of Websites Promoting Health Behaviors During Infancy: 6-Year Update of the Systematic Assessment",
journal="J Med Internet Res",
year="2022",
month="Oct",
day="7",
volume="24",
number="10",
pages="e38641",
keywords="breastfeeding",
keywords="bottle feeding",
keywords="websites",
keywords="web-based platform",
keywords="infant food",
keywords="readability",
keywords="accuracy",
keywords="consumer",
keywords="health information",
keywords="interactivity",
keywords="solid food",
keywords="quality",
keywords="grading",
keywords="comprehensibility",
keywords="infant",
keywords="baby",
keywords="babies",
keywords="feeding",
keywords="food",
keywords="eating",
keywords="nutrition",
keywords="health behavior",
keywords="web-based information",
keywords="health website",
keywords="sleep",
keywords="screen time",
keywords="rating",
abstract="Background: As of 2021, 89\% of the?Australian population are active internet users. Although the internet is widely used, there are concerns about the quality, accuracy, and credibility of health-related websites. A 2015 systematic assessment of infant feeding websites and apps available in Australia found that 61\% of websites were of poor quality and readability, with minimal coverage of infant feeding topics and lack of author credibility. Objective: We aimed to systematically assess the quality, interactivity, readability, and comprehensibility of information targeting infant health behaviors on websites globally and provide an update of the 2015 systematic assessment. Methods: Keywords related to infant milk feeding behaviors, solid feeding behaviors, active play, screen time, and sleep were used to identify websites targeting infant health behaviors on the Google search engine on Safari. The websites were assessed by a subset of the authors using predetermined criteria between July 2021 and February 2022 and assessed for information content based on the Australian Infant Feeding Guidelines and National Physical Activity Recommendations. The Suitability Assessment of Materials, Quality Component Scoring System, the Health-Related Website Evaluation Form, and the adherence to the Health on the Net code were used to evaluate the suitability and quality of information. Readability was assessed using 3 web-based readability tools. Results: Of the 450 websites screened, 66 were included based on the selection criteria and evaluated. Overall, the quality of websites was mostly adequate. Media-related sources, nongovernmental organizations, hospitals, and privately owned websites had the highest median quality scores, whereas university websites received the lowest median score (35\%). The information covered within the websites was predominantly poor: 91\% (60/66) of the websites received an overall score of ?74\% (mean 53\%, SD 18\%). The suitability of health information was mostly rated adequate for literacy demand, layout, and learning and motivation of readers. The median readability score for the websites was grade 8.5, which is higher than the government recommendations (<grade 8). Overall, 74\% (49/66) of the websites obtained a poor rating for interactivity, measuring active control, 2-way communication, and synchronicity. The most common features found on websites were social media links (61/66, 92\%), frequently asked questions (48/66, 73\%), and videos (44/66, 67\%). Only 14\% (9/66) of websites presented culturally responsive information. Conclusions: Quality, content, readability, and interactivity of websites promoting health behaviors during infancy ranged between poor and adequate. Since the 2015 systematic assessment, there was a slight improvement in the quality of websites but no difference in the Suitability Assessment of Materials rating and readability of information. There is a need for researchers and health care providers to leverage innovative web-based platforms to provide culturally competent evidence-based information based on government guidelines that are accessible to those with limited English proficiency. ",
doi="10.2196/38641",
url="https://www.jmir.org/2022/10/e38641",
url="http://www.ncbi.nlm.nih.gov/pubmed/36206031"
}


@Article{info:doi/10.2196/39274,
author="Korshakova, Elena
and Marsh, K. Jessecae
and Kleinberg, Samantha",
title="Health Information Sourcing and Health Knowledge Quality: Repeated Cross-sectional Survey",
journal="JMIR Form Res",
year="2022",
month="Sep",
day="28",
volume="6",
number="9",
pages="e39274",
keywords="health knowledge",
keywords="health information seeking",
keywords="information dissemination",
keywords="COVID-19",
keywords="online health information",
keywords="public health",
keywords="health literacy",
keywords="social media",
keywords="information quality",
keywords="infodemiology",
abstract="Background: People's health-related knowledge influences health outcomes, as this knowledge may influence whether individuals follow advice from their doctors or public health agencies. Yet, little attention has been paid to where people obtain health information and how these information sources relate to the quality of knowledge. Objective: We aim to discover what information sources people use to learn about health conditions, how these sources relate to the quality of their health knowledge, and how both the number of information sources and health knowledge change over time. Methods: We surveyed 200 different individuals at 12 time points from March through September 2020. At each time point, we elicited participants' knowledge about causes, risk factors, and preventative interventions for 8 viral (Ebola, common cold, COVID-19, Zika) and nonviral (food allergies, amyotrophic lateral sclerosis [ALS], strep throat, stroke) illnesses. Participants were further asked how they learned about each illness and to rate how much they trust various sources of health information. Results: We found that participants used different information sources to obtain health information about common illnesses (food allergies, strep throat, stroke) compared to emerging illnesses (Ebola, common cold, COVID-19, Zika). Participants relied mainly on news media, government agencies, and social media for information about emerging illnesses, while learning about common illnesses from family, friends, and medical professionals. Participants relied on social media for information about COVID-19, with their knowledge accuracy of COVID-19 declining over the course of the pandemic. The number of information sources participants used was positively correlated with health knowledge quality, though there was no relationship with the specific source types consulted. Conclusions: Building on prior work on health information seeking and factors affecting health knowledge, we now find that people systematically consult different types of information sources by illness type and that the number of information sources people use affects the quality of individuals' health knowledge. Interventions to disseminate health information may need to be targeted to where individuals are likely to seek out information, and these information sources differ systematically by illness type. ",
doi="10.2196/39274",
url="https://formative.jmir.org/2022/9/e39274",
url="http://www.ncbi.nlm.nih.gov/pubmed/35998198"
}


@Article{info:doi/10.2196/37286,
author="Abroms, C. Lorien
and Yom-Tov, Elad",
title="The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data",
journal="JMIR Infodemiology",
year="2022",
month="Sep",
day="14",
volume="2",
number="2",
pages="e37286",
keywords="health misinformation",
keywords="search engine",
keywords="internet search",
keywords="information boxes",
keywords="knowledge graph boxes",
keywords="misinformation",
keywords="health information",
keywords="Microsoft",
keywords="internet",
keywords="data",
keywords="symptoms",
keywords="results",
keywords="users",
keywords="medical",
keywords="Bing",
keywords="USA",
keywords="linear",
keywords="logistic",
keywords="regression",
keywords="web",
keywords="ads",
keywords="behavior",
abstract="Background: Search engines provide health information boxes as part of search results to address information gaps and misinformation for commonly searched symptoms. Few prior studies have sought to understand how individuals who are seeking information about health symptoms navigate different types of page elements on search engine results pages, including health information boxes. Objective: Using real-world search engine data, this study sought to investigate how users searching for common health-related symptoms with Bing interacted with health information boxes (info boxes) and other page elements. Methods: A sample of searches (N=28,552 unique searches) was compiled for the 17 most common medical symptoms queried on Microsoft Bing by users in the United States between September and November 2019. The association between the page elements that users saw, their characteristics, and the time spent on elements or clicks was investigated using linear and logistic regression. Results: The number of searches ranged by symptom type from 55 searches for cramps to 7459 searches for anxiety. Users searching for common health-related symptoms saw pages with standard web results (n=24,034, 84\%), itemized web results (n=23,354, 82\%), ads (n=13,171, 46\%), and info boxes (n=18,215, 64\%). Users spent on average 22 (SD 26) seconds on the search engine results page. Users who saw all page elements spent 25\% (7.1 s) of their time on the info box, 23\% (6.1 s) on standard web results, 20\% (5.7 s) on ads, and 10\% (10 s) on itemized web results, with significantly more time on the info box compared to other elements and the least amount of time on itemized web results. Info box characteristics such as reading ease and appearance of related conditions were associated with longer time on the info box. Although none of the info box characteristics were associated with clicks on standard web results, info box characteristics such as reading ease and related searches were negatively correlated with clicks on ads. Conclusions: Info boxes were attended most by users compared with other page elements, and their characteristics may influence future web searching. Future studies are needed that further explore the utility of info boxes and their influence on real-world health-seeking behaviors. ",
doi="10.2196/37286",
url="https://infodemiology.jmir.org/2022/2/e37286",
url="http://www.ncbi.nlm.nih.gov/pubmed/37113445"
}


@Article{info:doi/10.2196/37104,
author="Baharloo, Raheleh
and Principe, Jose
and Rashidi, Parisa
and Tighe, Patrick",
title="Long-Term Postoperative Pain Prediction Using Higher-Order Singular Value Decomposition of Intraoperative Physiological Responses: Prospective Cohort Study",
journal="JMIR Perioper Med",
year="2022",
month="Sep",
day="14",
volume="5",
number="1",
pages="e37104",
keywords="tensor decomposition",
keywords="multivariate-temporal decomposition",
keywords="long-term postoperative pain",
keywords="higher-order singular value decomposition",
keywords="SVD",
abstract="Background: Long-term postoperative pain (POP) and patient responses to pain relief medications are not yet fully understood. Although recent studies have developed an index for the nociception level of patients under general anesthesia based on multiple physiological parameters, it remains unclear whether these parameters correlate with long-term POP outcomes. Objective: This study aims to extract unbiased and interpretable descriptions of how the dynamics of physiological parameters change over time and across patients in response to surgical procedures and intraoperative medications using a multivariate-temporal analysis. We demonstrated that there is an association (correlation) between the main features of intraoperative physiological responses and long-term POP, which has a predictive value, even without claiming causality. Methods: We proposed a complex higher-order singular value decomposition method to accurately decompose patients' physiological responses into multivariate structures evolving over time. We used intraoperative vital signs of 175 patients from a mixed surgical cohort to extract three interconnected, low-dimensional, complex-valued descriptions of patients' physiological responses: multivariate factors, reflecting subphysiological parameters; temporal factors, reflecting common intrasurgery temporal dynamics; and patients' factors, describing interpatient changes in physiological responses. Results: Adoption of the complex higher-order singular value decomposition method allowed us to clarify the dynamic correlation structure included in the intraoperative physiological responses. Instantaneous phases of the complex-valued physiological responses of 242 patients within the subspace of principal descriptors enabled us to discriminate between mild and not-mild (moderate-severe) levels of pain at postoperative days 30 and 90. Following rotation of physiological responses before projection to align with the common multivariate-temporal dynamic, the method achieved an area under curve for postoperative day 30 and 90 outcomes of 0.81 and 0.89 for thoracic surgery, 0.87 and 0.83 for orthopedic surgery, 0.87 and 0.88 for urological surgery, 0.86 and 1 for colorectal surgery, 1 and 1 for transplant surgery, and 0.83 and 0.92 for pancreatic surgery, respectively. Conclusions: By categorizing patients into different surgical groups, we identified significant surgery-related principal descriptors. Each of them potentially encodes different surgical stimulation. The dynamics of patients' physiological responses to these surgical events were linked to long-term POP development. ",
doi="10.2196/37104",
url="https://periop.jmir.org/2022/1/e37104",
url="http://www.ncbi.nlm.nih.gov/pubmed/36103231"
}


@Article{info:doi/10.2196/29609,
author="Chiu, Yen-Lin
and Tsai, Chin-Chung
and Liang, Jyh-Chong",
title="Laypeople's Online Health Information Search Strategies and Use for Health-Related Problems: Cross-sectional Survey",
journal="J Med Internet Res",
year="2022",
month="Sep",
day="2",
volume="24",
number="9",
pages="e29609",
keywords="decision making",
keywords="eHealth literacy",
keywords="information search strategy",
keywords="internet",
keywords="patient",
keywords="information-seeking behavior",
keywords="laypeople",
keywords="online health information",
keywords="patient communication",
abstract="Background: With the increase in the use of the internet to search for health information about health-related problems, there is a need for health care professionals to better understand how their patients search for and use the online health information that may influence their medical decision making. Objective: The aims of this study are to explore laypeople's online health information search strategies and examine the relationships between their search strategies and utilization behavior of online health information. Methods: Two scales, namely match and elaboration, were used to measure patients' basic search strategies (ie, simple approach) and advanced search strategies (ie, integrative approach), respectively. In addition, the consultation scale was used to evaluate the participants' use of online health information to consult doctors and others. A total of 253 outpatients without university education were purposely selected and surveyed. The participants were outpatients at a university-affiliated teaching hospital. Partial least squares-structural equation modeling (PLS-SEM) was performed to analyze the measurement model to specify the measurement validation. In addition, the structure model of PLS-SEM was evaluated to examine the path correlations between variables and to execute interaction effect and curvilinear relationship analyses. Results: The results of the path correlation analysis by PLS-SEM showed that both elaboration strategy (path coefficient=0.55, P<.001) and match strategy (path coefficient=0.36, P<.001) were positively correlated with consultation on online health information with doctors and others. In addition, interaction effect and curvilinear relationship analyses indicated that there was a significant interaction effect between elaboration and match on consultation (path coefficient=--0.34, P<.001) and a significant curvilinear relationship between match and consultation (path coefficient=--0.09, P=.046). Conclusions: Increasing patients' exposure to online health information through both a simple search approach (ie, match strategy) and a complex search approach (ie, elaboration strategy) may lead them to appropriately use the information to consult doctors and others. However, the results of interaction effect and curvilinear relationship analyses highlighted the essential role of the elaboration strategy to properly locate, evaluate, and apply online health information. The findings of this study may help health care professionals better understand how to communicate with their patients through the health information on the internet. ",
doi="10.2196/29609",
url="https://www.jmir.org/2022/9/e29609",
url="http://www.ncbi.nlm.nih.gov/pubmed/36053561"
}


@Article{info:doi/10.2196/36322,
author="Arellano Carmona, Kimberly
and Chittamuru, Deepti
and Kravitz, L. Richard
and Ramondt, Steven
and Ram{\'i}rez, Susana A.",
title="Health Information Seeking From an Intelligent Web-Based Symptom Checker: Cross-sectional Questionnaire Study",
journal="J Med Internet Res",
year="2022",
month="Aug",
day="19",
volume="24",
number="8",
pages="e36322",
keywords="health information seeking",
keywords="health information",
keywords="information seeking",
keywords="information seeker",
keywords="information behavior",
keywords="artificial intelligence",
keywords="medical information system",
keywords="digital divide",
keywords="information inequality",
keywords="digital epidemiology",
keywords="symptom checker",
keywords="digital health",
keywords="eHealth",
keywords="online health information",
keywords="user demographic",
keywords="health information resource",
keywords="health information tool",
keywords="digital health assistant",
abstract="Background: The ever-growing amount of health information available on the web is increasing the demand for tools providing personalized and actionable health information. Such tools include symptom checkers that provide users with a potential diagnosis after responding to a set of probes about their symptoms. Although the potential for their utility is great, little is known about such tools' actual use and effects. Objective: We aimed to understand who uses a web-based artificial intelligence--powered symptom checker and its purposes, how they evaluate the experience of the web-based interview and quality of the information, what they intend to do with the recommendation, and predictors of future use. Methods: Cross-sectional survey of web-based health information seekers following the completion of a symptom checker visit (N=2437). Measures of comprehensibility, confidence, usefulness, health-related anxiety, empowerment, and intention to use in the future were assessed. ANOVAs and the Wilcoxon rank sum test examined mean outcome differences in racial, ethnic, and sex groups. The relationship between perceptions of the symptom checker and intention to follow recommended actions was assessed using multilevel logistic regression. Results: Buoy users were well-educated (1384/1704, 81.22\% college or higher), primarily White (1227/1693, 72.47\%), and female (2069/2437, 84.89\%). Most had insurance (1449/1630, 88.89\%), a regular health care provider (1307/1709, 76.48\%), and reported good health (1000/1703, 58.72\%). Three types of symptoms---pain (855/2437, 35.08\%), gynecological issues (293/2437, 12.02\%), and masses or lumps (204/2437, 8.37\%)---accounted for almost half (1352/2437, 55.48\%) of site visits. Buoy's top three primary recommendations split across less-serious triage categories: primary care physician in 2 weeks (754/2141, 35.22\%), self-treatment (452/2141, 21.11\%), and primary care in 1 to 2 days (373/2141, 17.42\%). Common diagnoses were musculoskeletal (303/2437, 12.43\%), gynecological (304/2437, 12.47\%) and skin conditions (297/2437, 12.19\%), and infectious diseases (300/2437, 12.31\%). Users generally reported high confidence in Buoy, found it useful and easy to understand, and said that Buoy made them feel less anxious and more empowered to seek medical help. Users for whom Buoy recommended ``Waiting/Watching'' or ``Self-Treatment'' had strongest intentions to comply, whereas those advised to seek primary care had weaker intentions. Compared with White users, Latino and Black users had significantly more confidence in Buoy (P<.05), and the former also found it significantly more useful (P<.05). Latino (odds ratio 1.96, 95\% CI 1.22-3.25) and Black (odds ratio 2.37, 95\% CI 1.57-3.66) users also had stronger intentions to discuss recommendations with a provider than White users. Conclusions: Results demonstrate the potential utility of a web-based health information tool to empower people to seek care and reduce health-related anxiety. However, despite encouraging results suggesting the tool may fulfill unmet health information needs among women and Black and Latino adults, analyses of the user base illustrate persistent second-level digital divide effects. ",
doi="10.2196/36322",
url="https://www.jmir.org/2022/8/e36322",
url="http://www.ncbi.nlm.nih.gov/pubmed/35984690"
}


@Article{info:doi/10.2196/29186,
author="Stemmer, Maya
and Parmet, Yisrael
and Ravid, Gilad",
title="Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study",
journal="J Med Internet Res",
year="2022",
month="Aug",
day="2",
volume="24",
number="8",
pages="e29186",
keywords="patient identification",
keywords="inflammatory bowel disease",
keywords="IBD",
keywords="user classification",
keywords="Twitter",
keywords="natural language processing",
keywords="NLP",
keywords="sentiment analysis",
abstract="Background: Patients use social media as an alternative information source, where they share information and provide social support. Although large amounts of health-related data are posted on Twitter and other social networking platforms each day, research using social media data to understand chronic conditions and patients' lifestyles is limited. Objective: In this study, we contributed to closing this gap by providing a framework for identifying patients with inflammatory bowel disease (IBD) on Twitter and learning from their personal experiences. We enabled the analysis of patients' tweets by building a classifier of Twitter users that distinguishes patients from other entities. This study aimed to uncover the potential of using Twitter data to promote the well-being of patients with IBD by relying on the wisdom of the crowd to identify healthy lifestyles. We sought to leverage posts describing patients' daily activities and their influence on their well-being to characterize lifestyle-related treatments. Methods: In the first stage of the study, a machine learning method combining social network analysis and natural language processing was used to automatically classify users as patients or not. We considered 3 types of features: the user's behavior on Twitter, the content of the user's tweets, and the social structure of the user's network. We compared the performances of several classification algorithms within 2 classification approaches. One classified each tweet and deduced the user's class from their tweet-level classification. The other aggregated tweet-level features to user-level features and classified the users themselves. Different classification algorithms were examined and compared using 4 measures: precision, recall, F1 score, and the area under the receiver operating characteristic curve. In the second stage, a classifier from the first stage was used to collect patients' tweets describing the different lifestyles patients adopt to deal with their disease. Using IBM Watson Service for entity sentiment analysis, we calculated the average sentiment of 420 lifestyle-related words that patients with IBD use when describing their daily routine. Results: Both classification approaches showed promising results. Although the precision rates were slightly higher for the tweet-level approach, the recall and area under the receiver operating characteristic curve of the user-level approach were significantly better. Sentiment analysis of tweets written by patients with IBD identified frequently mentioned lifestyles and their influence on patients' well-being. The findings reinforced what is known about suitable nutrition for IBD as several foods known to cause inflammation were pointed out in negative sentiment, whereas relaxing activities and anti-inflammatory foods surfaced in a positive context. Conclusions: This study suggests a pipeline for identifying patients with IBD on Twitter and collecting their tweets to analyze the experimental knowledge they share. These methods can be adapted to other diseases and enhance medical research on chronic conditions. ",
doi="10.2196/29186",
url="https://www.jmir.org/2022/8/e29186",
url="http://www.ncbi.nlm.nih.gov/pubmed/35917151"
}


@Article{info:doi/10.2196/38004,
author="Fenton, H. Susan
and Marc, T. David
and Kennedy, Angela
and Hamada, Debra
and Hoyt, Robert
and Lalani, Karima
and Renda, Connie
and Reynolds, B. Rebecca",
title="Aligning the American Health Information Management Association Entry-level Curricula Competencies and Career Map With Industry Job Postings: Cross-sectional Study",
journal="JMIR Med Educ",
year="2022",
month="Jul",
day="7",
volume="8",
number="3",
pages="e38004",
keywords="health information management",
keywords="health workforce",
keywords="healthcare industry",
keywords="natural language processing",
keywords="medical education",
keywords="professional education",
keywords="job recruitment",
keywords="job website",
keywords="web scraping",
keywords="data mining",
abstract="Background: The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA's HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA's career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands. Objective: The primary aim of this study is to analyze HIM job postings on a US national job recruiting website to determine whether the job postings align with recognized HIM domains, while the secondary aim is to evaluate the AHIMA career map to determine whether it aligns with the job postings. Methods: A national job recruitment website was mined electronically (web scraping) using the search term ``health information management.'' This cross-sectional inquiry evaluated job advertisements during a 2-week period in 2021. After the exclusion criteria, 691 job postings were analyzed. Data were evaluated with descriptive statistics and natural language processing (NLP). Soft cosine measures (SCM) were used to determine correlations between job postings and the AHIMA career map, curricular competencies, and curricular considerations. ANOVA was used to determine statistical significance. Results: Of all the job postings, 29\% (140/691) were in the Southeast, followed by the Midwest (140/691, 20\%), West (131/691,19\%), Northeast (94/691, 14\%), and Southwest (73/691, 11\%). The educational levels requested were evenly distributed between high school diploma (219/691, 31.7\%), associate degree (269/691, 38.6\%), or bachelor's degree (225/691, 32.5\%). A master's degree was requested in only 8\% (52/691) of the postings, with 72\% (42/58) preferring one and 28\% (16/58) requiring one. A Registered Health Information Technologist (RHIT) credential was the most commonly requested (207/691, 29.9\%) in job postings, followed by Registered Health Information Administrator (RHIA; 180/691, 26\%) credential. SCM scores were significantly higher in the informatics category compared to the coding and revenue cycle (P=.006) and data analytics categories (P<.001) but not significantly different from the information governance category (P=.85). The coding and revenue cycle category had a significantly higher SCM score compared to the data analytics category (P<.001). Additionally, the information governance category was significantly higher than the data analytics category (P<.001). SCM scores were significantly different between each competency category, except there were no differences in the average SCM score between the information protection and revenue cycle management categories (P=.96) and the information protection and data structure, content, and information governance categories (P=.31). Conclusions: Industry job postings primarily sought degrees, with a master's degree a distant fourth. NLP analysis of job postings suggested that the correlation between the informatics category and job postings was higher than that of the coding, revenue cycle, and data analytics categories. ",
doi="10.2196/38004",
url="https://mededu.jmir.org/2022/3/e38004",
url="http://www.ncbi.nlm.nih.gov/pubmed/35584188"
}


@Article{info:doi/10.2196/27310,
author="Deiner, S. Michael
and Kaur, Gurbani
and McLeod, D. Stephen
and Schallhorn, M. Julie
and Chodosh, James
and Hwang, H. Daniel
and Lietman, M. Thomas
and Porco, C. Travis",
title="A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study",
journal="J Med Internet Res",
year="2022",
month="Jul",
day="5",
volume="24",
number="7",
pages="e27310",
keywords="diurnal eye conditions",
keywords="hebdomadal",
keywords="online search",
keywords="web-based search",
keywords="eye conditions",
keywords="infodemiology",
keywords="dry eye",
keywords="conjunctivitis",
keywords="pink eye",
keywords="information seeking",
keywords="vision",
abstract="Background: Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients' eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations. Objective: To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other. Methods: We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google's search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant. Results: Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, ``pink eye'' showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, ``dry eyes'' had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning. Conclusions: The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions. ",
doi="10.2196/27310",
url="https://www.jmir.org/2022/7/e27310",
url="http://www.ncbi.nlm.nih.gov/pubmed/35537041"
}


@Article{info:doi/10.2196/34979,
author="Leung, I. Tiffany
and Kuhn, Tobias
and Dumontier, Michel",
title="Representing Physician Suicide Claims as Nanopublications: Proof-of-Concept Study Creating Claim Networks",
journal="JMIRx Med",
year="2022",
month="Jul",
day="1",
volume="3",
number="3",
pages="e34979",
keywords="physician suicide",
keywords="suicide",
keywords="suicide prevention",
keywords="physician well-being",
keywords="physician mental health",
keywords="nanopublication",
keywords="physician",
keywords="doctor",
keywords="mental health",
keywords="semantic publishing",
keywords="bibliometrics",
keywords="claim network",
keywords="information distortion",
keywords="misinformation",
abstract="Background: In the poorly studied field of physician suicide, various factors can contribute to misinformation or information distortion, which in turn can influence evidence-based policies and prevention of suicide in this unique population. Objective: The aim of this paper is to use nanopublications as a scientific publishing approach to establish a citation network of claims in peer-reviewed publications about the rate of suicide among US physicians. Methods: A list of articles from a previously published scoping literature review on physician suicide was used to identify those articles that commented on or investigated suicidal behaviors of physician populations, including students, postgraduate trainees, and practicing physicians. The included articles were from peer-reviewed publications and asserted a claim about the annual rate of physician suicide. Manual data extraction was performed to collect article (or resource) type, title, authors, digital object identifier or URI, publication year, claim (about annual physician suicide rate), data of last access of the article (eg, for a webpage), and citations supporting the claim. Additional articles, websites, or other links were only added to the set of claims if they were cited by a peer-reviewed article already included in the data set. A nanopublication was created for each article or resource using Nanobench with an investigator-developed literature-based claim nanopublication template. Results: A set of 49 claims concerning the rate of US physician suicide was represented as nanopublications. Analysis of the claim network revealed that (1) the network is not fully connected, (2) no single primary source of the claim could be identified, and (3) all end-point citations had a claim with no further citation, had no apparent claim, or could not be accessed to verify the claim. The nanopublication strategy also enabled the capture of variant claims published on a website. Conclusions: Nanopublications remain to be adopted in broader scientific publishing in medicine, especially in publishing about physician mental health and suicide. This proof-of-concept study highlights an opportunity for more coordinated research efforts in the subject of physician suicide. Our work integrates these various claims and enables the verification of nonauthoritative assertions, thereby better equipping researchers to advance evidence-based knowledge and to make informed statements in the advocacy of physician suicide prevention. Representing physician suicide rate claims as nanopublications can be extended and improved in future work. ",
doi="10.2196/34979",
url="https://med.jmirx.org/2022/3/e34979",
url="http://www.ncbi.nlm.nih.gov/pubmed/27725715"
}


@Article{info:doi/10.2196/34345,
author="El Sherif, Reem
and Pluye, Pierre
and Ibekwe, Fidelia",
title="Contexts and Outcomes of Proxy Online Health Information Seeking: Mixed Studies Review With Framework Synthesis",
journal="J Med Internet Res",
year="2022",
month="Jun",
day="24",
volume="24",
number="6",
pages="e34345",
keywords="online health information",
keywords="information seeking behavior",
keywords="proxy information seeking",
keywords="surrogate seekers",
keywords="information outcomes",
keywords="social support",
keywords="health information",
keywords="online information",
abstract="Background: High-quality online health information (OHI) can reduce unnecessary visits to health professionals and improve health. One of the ways that people use OHI is to support others with health conditions through proxy OHI seeking. Members of a person's social circle may help them overcome information-seeking barriers and illness challenges. There are several models on proxy information seeking. Yet, we know little about the use and outcomes of OHI on behalf of someone else. Objective: The objectives of this paper are to explore and revise a framework on the context and outcomes of proxy OHI seeking Methods: We conducted a mixed studies literature review integrating qualitative and quantitative evidence with thematic analysis of the findings of 28 studies, followed by framework synthesis incorporating the derived themes. Results: We explored 4 main themes: (1) characteristics of proxy seekers, (2) context of proxy OHI seeking, (3) use of OHI to provide social support, and (4) outcomes of proxy OHI seeking. Our conceptual framework incorporates these themes and builds on previous work. Conclusions: By better understanding how people use information together, information providers can adapt the information to meet all users' needs. ",
doi="10.2196/34345",
url="https://www.jmir.org/2022/6/e34345",
url="http://www.ncbi.nlm.nih.gov/pubmed/35749210"
}


@Article{info:doi/10.2196/38407,
author="B{\'a}lint, Andr{\'a}s
and Wimmer, Wilhelm
and Caversaccio, Marco
and Weder, Stefan",
title="Neural Activity During Audiovisual Speech Processing: Protocol For a Functional Neuroimaging Study",
journal="JMIR Res Protoc",
year="2022",
month="Jun",
day="21",
volume="11",
number="6",
pages="e38407",
keywords="hearing loss",
keywords="brain plasticity",
keywords="functional near-infrared spectroscopy (fNIRS)",
keywords="cochlear implant",
keywords="neuroimaging",
keywords="speech understanding",
keywords="comprehension",
keywords="speech",
keywords="brain activation",
keywords="brain activity",
keywords="hearing impairment",
keywords="cortical activation",
keywords="neural",
keywords="brain",
keywords="protocol",
keywords="spectroscopy",
keywords="cochlear",
keywords="hearing",
abstract="Background: Functional near-infrared spectroscopy (fNIRS) studies have demonstrated associations between hearing outcomes after cochlear implantation and plastic brain changes. However, inconsistent results make it difficult to draw conclusions. A major problem is that many variables need to be controlled. To gain further understanding, a careful preparation and planning of such a functional neuroimaging task is key. Objective: Using fNIRS, our main objective is to develop a well-controlled audiovisual speech comprehension task to study brain activation in individuals with normal hearing and hearing impairment (including cochlear implant users). The task should be deductible from clinically established tests, induce maximal cortical activation, use optimal coverage of relevant brain regions, and be reproducible by other research groups. Methods: The protocol will consist of a 5-minute resting state and 2 stimulation periods that are 12 minutes each. During the stimulation periods, 13-second video recordings of the clinically established Oldenburg Sentence Test (OLSA) will be presented. Stimuli will be presented in 4 different modalities: (1) speech in quiet, (2) speech in noise, (3) visual only (ie, lipreading), and (4) audiovisual speech. Each stimulus type will be repeated 10 times in a counterbalanced block design. Interactive question windows will monitor speech comprehension during the task. After the measurement, we will perform a 3D scan to digitize optode positions and verify the covered anatomical locations. Results: This paper reports the study protocol. Enrollment for the study started in August 2021. We expect to publish our first results by the end of 2022. Conclusions: The proposed audiovisual speech comprehension task will help elucidate neural correlates to speech understanding. The comprehensive study will have the potential to provide additional information beyond the conventional clinical standards about the underlying plastic brain changes of a hearing-impaired person. It will facilitate more precise indication criteria for cochlear implantation and better planning of rehabilitation. International Registered Report Identifier (IRRID): DERR1-10.2196/38407 ",
doi="10.2196/38407",
url="https://www.researchprotocols.org/2022/6/e38407",
url="http://www.ncbi.nlm.nih.gov/pubmed/35727624"
}


@Article{info:doi/10.2196/34708,
author="Rao, Nikita
and Tighe, L. Elizabeth
and Feinberg, Iris",
title="The Dispersion of Health Information--Seeking Behavior and Health Literacy in a State in the Southern United States: Cross-sectional Study",
journal="JMIR Form Res",
year="2022",
month="Jun",
day="15",
volume="6",
number="6",
pages="e34708",
keywords="health information--seeking behavior",
keywords="health literacy",
keywords="digital literacy",
keywords="information retrieval",
keywords="health literacy questionnaire",
abstract="Background: The transmission of health information from in-person communication to web-based sources has changed over time. Patients can find, understand, and use their health information without meeting a health care provider and are able to participate more in their health care management. In recent years, the internet has emerged as the primary source of health information, although clinical providers remain the most credible source. The ease of access, anonymity, and busy schedules may be motivating factors to seek health information on the web. Social media has surfaced as a popular source of health information, as it can provide news in real time. The increase in the breadth and depth of health information available on the web has also led to a plethora of misinformation, and individuals are often unable to discern facts from fiction. Competencies in health literacy (HL) can help individuals better understand health information and enhance patient decision-making, as adequate HL is a precursor to positive health information--seeking behaviors (HISBs). Several factors such as age, sex, and socioeconomic status are known to moderate the association between HL and HISBs. Objective: In this study, we aimed to examine the relationship between HL and HISBs in individuals living in a southern state in the United States by considering different demographic factors. Methods: Participants aged ?18 years were recruited using Qualtrics Research Services and stratified to match the statewide demographic characteristics of race and age. Demographics and source and frequency of health information were collected. The Health Literacy Questionnaire was used to collect self-reported HL experiences. SPSS (version 27; IBM Corp) was used for the analysis. Results: A total of 520 participants met the criteria and completed the survey (mean age 36.3, SD 12.79 years). The internet was cited as the most used source of health information (mean 2.41, SD 0.93). Females are more likely to seek health information from physicians than males (r=0.121; P=.006). Older individuals are less likely to seek health information from the internet (r=?0.108; P=.02), social media (r=?0.225; P<.001), and friends (r=?0.090; P=.045) than younger individuals. Cluster analysis demonstrated that individuals with higher levels of HISBs were more likely to seek information from multiple sources than those with lower levels of HISBs (mean range 3.05-4.09, SD range 0.57-0.66; P<.001). Conclusions: Age and sex are significantly associated with HISB. Older adults may benefit from web-based resources to monitor their health conditions. Higher levels of HL are significantly associated with greater HISB. Targeted strategies to improve HISB among individuals with lower levels of HL may improve their access, understanding, and use of health information. ",
doi="10.2196/34708",
url="https://formative.jmir.org/2022/6/e34708",
url="http://www.ncbi.nlm.nih.gov/pubmed/35704357"
}


@Article{info:doi/10.2196/31148,
author="Drtilova, Hana
and Machackova, Hana
and Smahelova, Martina",
title="Evaluation of Web-Based Health Information From the Perspective of Women With Eating Disorders: Thematic Analysis",
journal="J Med Internet Res",
year="2022",
month="Jun",
day="13",
volume="24",
number="6",
pages="e31148",
keywords="eating disorders",
keywords="web-based health information",
keywords="Czech women",
abstract="Background: Users with experience of eating disorders use the internet as a source of information, whether for prorecovery activities (such as web-based treatment, looking for information, support, and sharing) or activities that promote eating disorder behavior as a desirable lifestyle choice (such as pro--eating disorder communities and reading and creating pro--eating disorder posts). Their assessment of web-based eating disorder--related information is crucial for understanding the context of the illness and for health professionals and their web-based interventions. Objective: This study aimed to understand the criteria young women with the experience of eating disorders use in evaluating eating disorder--related web-based information and what eating disorder--related characteristics of these women are involved in their evaluation. Methods: We analyzed 30 semistructured individual interviews with Czech women aged 16 to 28 years with past or present eating disorder experience using a qualitative approach. Thematic analysis was adopted as an analytical tool. Results: The specifics of eating disorder phases (the disorder stage and the treatment process) emerged as important aspects in the process of information assessment. Other specific characteristics of respondents (eg, motivation, abilities, and resources) addressed how the respondents arrived at certain web-based information and how they evaluated it. In addition, the respondents described some content cues as features of information (eg, novelty and social information pooling). Another finding is that other users' attitudes, experiences, activities, and personal features are involved in the information evaluation of these users and the information presented by them. Finally, the respondents evaluated the websites' visual look and graphic components. Conclusions: This study shows that web-based information evaluation reported by women with experience of eating disorders is a complex process. The assessment is influenced by current personal characteristics related to the illness (mainly the motivation for maintaining or curing the eating disorder) using cues associated with information content, other users, and website look. The study findings have important implications for health professionals, who should ask their clients questions about web-based communities and their needs to understand what information and sources they choose. ",
doi="10.2196/31148",
url="https://www.jmir.org/2022/6/e31148",
url="http://www.ncbi.nlm.nih.gov/pubmed/35699984"
}


@Article{info:doi/10.2196/34594,
author="Kulkarni, Vishnutheertha
and Okoye, A. Ginette
and Garza, A. Luis
and Wongvibulsin, Shannon",
title="Geospatial Heterogeneity of Hidradenitis Suppurativa Searches in the United States: Infodemiology Study of Google Search Data",
journal="JMIR Dermatol",
year="2022",
month="Jun",
day="9",
volume="5",
number="2",
pages="e34594",
keywords="hidradenitis suppurativa",
keywords="infodemiology",
keywords="internet",
keywords="digital dermatoepidemiology",
keywords="epidemiology",
keywords="big data",
keywords="dermatology",
doi="10.2196/34594",
url="https://derma.jmir.org/2022/2/e34594",
url="http://www.ncbi.nlm.nih.gov/pubmed/37632873"
}


@Article{info:doi/10.2196/32723,
author="Eke, Ransome
and Jannat, Tul Khadiza
and Yang, Thomas Xin
and Parton, M. Jason",
title="Disparity and Factors Associated With Internet Health Information Seeking Among US Adults Living With Diabetes Mellitus: Cross-sectional Study",
journal="J Med Internet Res",
year="2022",
month="May",
day="30",
volume="24",
number="5",
pages="e32723",
keywords="diabetes",
keywords="internet",
keywords="information seeking",
keywords="adults living with diabetes",
keywords="predictors",
keywords="racial disparity",
keywords="Health Information National Trends Survey",
keywords="race",
keywords="ethnicity",
keywords="chronic conditions",
keywords="self-management",
keywords="internet health information",
keywords="mobile phone",
abstract="Background: Many patients with chronic medical conditions search the internet to obtain medical advice and health information to improve their health condition and quality of life. Diabetes is a common chronic disease that disproportionately affects different race and ethnicity groups in the United States. In the existing literature on the popularity of internet health information seeking among persons with a chronic medical condition, there are limited data on US adults living with diabetes. Objective: This study aims to examine the factors associated with internet health information seeking among US adults living with diabetes and whether there is a disparity in internet health information seeking stratified by race and ethnicity. Methods: We conducted a cross-sectional study using the Health Information National Trends Survey data from 2017 to 2020. We selected our study sample based on respondents' reports on whether they were told they had diabetes, and our primary outcome was internet health information--seeking behavior. We used 2 multivariable logistic regression models to examine the effects of sociodemographic factors and other covariates on the internet health information--seeking behavior of adults with diabetes. Jackknife replicate weights were used to provide bias-corrected variance estimates. Results: Our study sample included 2903 adults who self-reported that they had diabetes. In total, 60.08\% (1744/2903) were non-Hispanic White individuals, 46.88\% (1336/2850) were men, and 64\% (1812/2831) had some college or graduate education. The prevalence of internet health information seeking in this population was 64.49\% (1872/2903), and the main factors associated with internet health information seeking included education level (some college vs less than high school: odds ratio [OR] 1.42, 95\% CI 1.44-1.88; and college graduate or higher vs less than high school: OR 2.50, 95\% CI 1.79-3.50), age (age group ?65 years vs age group 18-44 years: OR 0.46, 95\% CI 0.34-0.63), and household income level (P<.001). In addition, we found significant differences in the effects of predictors stratified by race. Conclusions: The findings from this study suggest that internet health information seeking is common among US adults living with diabetes. Internet health information could influence the relationship between health care providers and adults living with diabetes and improve their self-management and quality of life. ",
doi="10.2196/32723",
url="https://www.jmir.org/2022/5/e32723",
url="http://www.ncbi.nlm.nih.gov/pubmed/35635741"
}


@Article{info:doi/10.2196/36835,
author="Gordejeva, Jelizaveta
and Zowalla, Richard
and Pobiruchin, Monika
and Wiesner, Martin",
title="Readability of English, German, and Russian Disease-Related Wikipedia Pages: Automated Computational Analysis",
journal="J Med Internet Res",
year="2022",
month="May",
day="16",
volume="24",
number="5",
pages="e36835",
keywords="readability",
keywords="health literacy",
keywords="health education",
keywords="Wikipedia",
abstract="Background: Wikipedia is a popular encyclopedia for health- and disease-related information in which patients seek advice and guidance on the web. Yet, Wikipedia articles can be unsuitable as patient education materials, as investigated in previous studies that analyzed specific diseases or medical topics with a comparatively small sample size. Currently, no data are available on the average readability levels of all disease-related Wikipedia pages for the different localizations of this particular encyclopedia. Objective: This study aimed to analyze disease-related Wikipedia pages written in English, German, and Russian using well-established readability metrics for each language. Methods: Wikipedia database snapshots and Wikidata metadata were chosen as resources for data collection. Disease-related articles were retrieved separately for English, German, and Russian starting with the main concept of Human Diseases and Disorders (German: Krankheit; Russian: ??????????? ????????). In the case of existence, the corresponding International Classification of Diseases, Tenth Revision (ICD-10), codes were retrieved for each article. Next, the raw texts were extracted and readability metrics were computed. Results: The number of articles included in this study for English, German, and Russian Wikipedia was n=6127, n=6024, and n=3314, respectively. Most disease-related articles had a Flesch Reading Ease (FRE) score <50.00, signaling difficult or very difficult educational material (English: 5937/6125, 96.93\%; German: 6004/6022, 99.7\%; Russian: 2647/3313, 79.9\%). In total, 70\% (7/10) of the analyzed articles could be assigned an ICD-10 code with certainty (English: 4235/6127, 69.12\%; German: 4625/6024, 76.78\%; Russian: 2316/3314, 69.89\%). For articles with ICD-10 codes, the mean FRE scores were 28.69 (SD 11.00), 20.33 (SD 9.98), and 38.54 (SD 13.51) for English, German, and Russian, respectively. A total of 9 English ICD-10 chapters (11 German and 10 Russian) showed significant differences: chapter F (FRE 23.88, SD 9.95; P<.001), chapter E (FRE 25.14, SD 9.88; P<.001), chapter H (FRE 30.04, SD 10.57; P=.049), chapter I (FRE 30.05, SD 9.07; P=.04), chapter M (FRE 31.17, 11.94; P<.001), chapter T (FRE 32.06, SD 10.51; P=.001), chapter A (FRE 32.63, SD 9.25; P<.001), chapter B (FRE 33.24, SD 9.07; P<.001), and chapter S (FRE 39.02, SD 8.22; P<.001). Conclusions: Disease-related English, German, and Russian Wikipedia articles cannot be recommended as patient education materials because a major fraction is difficult or very difficult to read. The authors of Wikipedia pages should carefully revise existing text materials for readers with a specific interest in a disease or its associated symptoms. Special attention should be given to articles on mental, behavioral, and neurodevelopmental disorders (ICD-10 chapter F) because these articles were most difficult to read in comparison with other ICD-10 chapters. Wikipedia readers should be supported by editors providing a short and easy-to-read summary for each article. ",
doi="10.2196/36835",
url="https://www.jmir.org/2022/5/e36835",
url="http://www.ncbi.nlm.nih.gov/pubmed/35576562"
}


@Article{info:doi/10.2196/29618,
author="Kubb, Christian
and Foran, M. Heather",
title="Online Health Information Seeking for Self and Child: An Experimental Study of Parental Symptom Search",
journal="JMIR Pediatr Parent",
year="2022",
month="May",
day="9",
volume="5",
number="2",
pages="e29618",
keywords="online health information seeking",
keywords="digital health",
keywords="parents",
keywords="parent-child",
keywords="eHealth literacy",
keywords="human computer interaction",
keywords="mobile phone",
abstract="Background: Parents often search the web for health-related information for themselves or on behalf of their children, which may impact their health-related decision-making and behaviors. In particular, searching for somatic symptoms such as headaches, fever, or fatigue is common. However, little is known about how psychological and relational factors relate to the characteristics of successful symptom-related internet searches. To date, few studies have used experimental designs that connect participant subjective search evaluation with objective search behavior metrics. Objective: This study aimed to examine the features of web-based health-related search behaviors based on video-coded observational data, to investigate which psychological and relational factors are related to successful symptom search appraisal, and to examine the differences in search-related outcomes among self-seekers and by-proxy seekers. Methods: In a laboratory setting, parents living in Austria (N=46) with a child aged between 0 and 6 years were randomized to search their own (n=23, 50\%) or their child's (n=23, 50\%) most recent somatic symptom on the web. Web-based activity was recorded and transcribed. Health anxiety, eHealth literacy, attitude toward web-based health information, relational variables, state of stress, participants' search appraisals, and quantitative properties of the search session were assessed. Differences in search appraisals and search characteristics among parents who searched for themselves or their children were examined. Results: Across both groups, searches were carried out for 17 different symptom clusters. Almost all parents started with Google (44/46, 96\%), and a majority used initial elaborated key phrases with >1 search keyword (38/45, 84\%) and performed on average 2.95 (SD 1.83) search queries per session. Search success was negatively associated with health anxiety (rs=?0.39, P=.01), stress after the search (rs=?0.33, P=.02), and the number of search queries (rs=?0.29, P=.04) but was not significantly associated with eHealth literacy (rs=0.22, P=.13). Of note, eHealth literacy was strongly and positively correlated with satisfaction during the search (rs=0.50, P<.001) but did not significantly correlate with search characteristics as measured by search duration (rs=0.08, P=.56), number of performed search queries (rs=0.20, P=.17), or total clicks (rs=0.14, P=.32). No differences were found between parents searching for their own symptoms and parents searching for their child's symptoms. Conclusions: This study provides exploratory findings regarding relevant dimensions of appraisals for symptom-based information seeking on the web. Consistent with previous literature, health anxiety was found to be associated with poorer search evaluation. Contrary to expectations, eHealth literacy was related neither to search success nor to search characteristics. Interestingly, we did not find significant differences between self-seekers and by-proxy seekers, suggesting similar search and evaluation patterns in our sample. Further research with larger samples is needed to identify and evaluate guidelines for enhanced web-based health information seeking among parents and the general public. ",
doi="10.2196/29618",
url="https://pediatrics.jmir.org/2022/2/e29618",
url="http://www.ncbi.nlm.nih.gov/pubmed/35532970"
}


@Article{info:doi/10.2196/34042,
author="Lin, Yuchen
and Lemos, Martin
and Neuschaefer-Rube, Christiane",
title="Digital Health and Learning in Speech-Language Pathology, Phoniatrics, and Otolaryngology: Survey Study for Designing a Digital Learning Toolbox App",
journal="JMIR Med Educ",
year="2022",
month="Apr",
day="27",
volume="8",
number="2",
pages="e34042",
keywords="digital learning",
keywords="mLearning",
keywords="mHealth",
keywords="speech-language pathology",
keywords="phoniatrics",
keywords="otolaryngology",
keywords="communication disorders",
keywords="mobile phone",
abstract="Background: The digital age has introduced opportunities and challenges for clinical education and practice caused by infinite incoming information and novel technologies for health. In the interdisciplinary field of communication sciences and disorders (CSD), engagement with digital topics has emerged slower than in other health fields, and effective strategies for accessing, managing, and focusing on digital resources are greatly needed. Objective: We aimed to conceptualize and investigate preferences of stakeholders regarding a digital learning toolbox, an app containing a library of current resources for CSD. This cross-sectional survey study conducted in German-speaking countries investigated professional and student perceptions and preferences regarding such an app's features, functions, content, and associated concerns. Methods: An open web-based survey was disseminated to professionals and students in the field of CSD, including speech-language pathologists (SLPs; German: Logop{\"a}d*innen), speech-language pathology students, phoniatricians, otolaryngologists, and medical students. Insights into preferences and perceptions across professions, generations, and years of experience regarding a proposed app were investigated. Results: Of the 164 participants, an overwhelming majority (n=162, 98.8\%) indicated readiness to use such an app, and most participants (n=159, 96.9\%) perceived the proposed app to be helpful. Participants positively rated app functions that would increase utility (eg, tutorial, quality rating function, filters based on content or topic, and digital format); however, they had varied opinions regarding an app community feature. Regarding app settings, most participants rated the option to share digital resources through social media links (144/164, 87.8\%), receive and manage push notifications (130/164, 79.3\%), and report technical issues (160/164, 97.6\%) positively. However, significant variance was noted across professions (H3=8.006; P=.046) and generations (H3=9.309; P=.03) regarding a username-password function, with SLPs indicating greater perceived usefulness in comparison to speech-language pathology students (P=.045), as was demonstrated by Generation X versus Generation Z (P=.04). Participants perceived a range of clinical topics to be important; however, significant variance was observed across professions, between physicians and SLPs regarding the topic of diagnostics (H3=9.098; P=.03) and therapy (H3=21.236; P<.001). Concerns included technical challenges, data protection, quality of the included resources, and sustainability of the proposed app. Conclusions: This investigation demonstrated that professionals and students show initial readiness to engage in the co-design and use of an interdisciplinary digital learning toolbox app. Specifically, this app could support effective access, sharing, evaluation, and knowledge management in a digital age of rapid change. Formalized digital skills education in the field of CSD is just a part of the solution. It will be crucial to explore flexible, adaptive strategies collaboratively for managing digital resources and tools to optimize targeted selection and use of relevant, high-quality evidence in a world of bewildering data. ",
doi="10.2196/34042",
url="https://mededu.jmir.org/2022/2/e34042",
url="http://www.ncbi.nlm.nih.gov/pubmed/35475980"
}


@Article{info:doi/10.2196/28114,
author="Nam, Seojin
and Kim, Donghun
and Jung, Woojin
and Zhu, Yongjun",
title="Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="22",
volume="24",
number="4",
pages="e28114",
keywords="deep learning",
keywords="scientometric analysis",
keywords="research publications",
keywords="research landscape",
keywords="research collaboration",
keywords="knowledge diffusion",
abstract="Background: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. Objective: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. Methods: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. Results: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. Conclusions: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work. ",
doi="10.2196/28114",
url="https://www.jmir.org/2022/4/e28114",
url="http://www.ncbi.nlm.nih.gov/pubmed/35451980"
}


@Article{info:doi/10.2196/34072,
author="Langnickel, Lisa
and Podorskaja, Daria
and Fluck, Juliane",
title="Pre2Pub---Tracking the Path From Preprint to Journal Article: Algorithm Development and Validation",
journal="J Med Internet Res",
year="2022",
month="Apr",
day="8",
volume="24",
number="4",
pages="e34072",
keywords="preprints",
keywords="information retrieval",
keywords="COVID-19",
keywords="metadata",
keywords="BERT",
keywords="Bidirectional Encoder Representations from Transformers",
abstract="Background: The current COVID-19 crisis underscores the importance of preprints, as they allow for rapid communication of research results without delay in review. To fully integrate this type of publication into library information systems, we developed preview: a publicly available, central search engine for COVID-19--related preprints, which clearly distinguishes this source from peer-reviewed publications. The relationship between the preprint version and its corresponding journal version should be stored as metadata in both versions so that duplicates can be easily identified and information overload for researchers is reduced. Objective: In this work, we investigated the extent to which the relationship information between preprint and corresponding journal publication is present in the published metadata, how it can be further completed, and how it can be used in preVIEW to identify already republished preprints and filter those duplicates in search results. Methods: We first analyzed the information content available at the preprint servers themselves and the information that can be retrieved via Crossref. Moreover, we developed the algorithm Pre2Pub to find the corresponding reviewed article for each preprint. We integrated the results of those different resources into our search engine preVIEW, presented the information in the result set overview, and added filter options accordingly. Results: Preprints have found their place in publication workflows; however, the link from a preprint to its corresponding journal publication is not completely covered in the metadata of the preprint servers or in Crossref. Our algorithm Pre2Pub is able to find approximately 16\% more related journal articles with a precision of 99.27\%. We also integrate this information in a transparent way within preVIEW so that researchers can use it in their search. Conclusions: Relationships between the preprint version and its journal version is valuable information that can help researchers finding only previously unknown information in preprints. As long as there is no transparent and complete way to store this relationship in metadata, the Pre2Pub algorithm is a suitable extension to retrieve this information. ",
doi="10.2196/34072",
url="https://www.jmir.org/2022/4/e34072",
url="http://www.ncbi.nlm.nih.gov/pubmed/35285808"
}


@Article{info:doi/10.2196/30687,
author="Muhamad, Asiah Nor
and Selvarajah, Vinesha
and Dharmaratne, Anuja
and Inthiran, Anushia
and Mohd Dali, Soleha Nor
and Chaiyakunapruk, Nathorn
and Lai, Ming Nai",
title="Online Searching as a Practice for Evidence-Based Medicine in the Neonatal Intensive Care Unit, University of Malaya Medical Center, Malaysia: Cross-sectional Study",
journal="JMIR Form Res",
year="2022",
month="Apr",
day="6",
volume="6",
number="4",
pages="e30687",
keywords="evidence-based practice",
keywords="online information searching",
keywords="information retrieval",
keywords="information seeking",
keywords="clinical setting",
abstract="Background: The use of the internet for research is essential in the practice of evidence-based medicine. The online search habits of medical practitioners in clinical settings, particularly from direct observation, have received little attention. Objective: The goal of the research is to explore online searching for information as an evidence-based practice among medical practitioners. Methods: A cross-sectional study was conducted to evaluate the clinical teams' use of evidence-based practice when making clinical decisions for their patients' care. Data were collected through online searches from 2015 to 2018.  Participants were medical practitioners and medical students in a Malaysian public teaching hospital's neonatal intensive care unit who performed online searches to find answers to clinical questions that arose during ward rounds. Results: In search sessions conducted by the participants, 311 queries were observed from 2015 to 2018. Most participants (34/47, 72\%) were house officers and medical students. Most of the searches were conducted by house officers (51/99, 52\%) and medical students (32/99, 32\%). Most searches (70/99, 71\%) were directed rather than self-initiated, and 90\% (89/99) were completed individually rather than collaboratively. Participants entered an average of 4 terms in each query; three-quarters of the queries yielded relevant evidence, with two-thirds yielding more than one relevant source of evidence. Conclusions: Our findings suggest that junior doctors and medical students need more training in evidence-based medicine skills such as clinical question formulation and online search techniques for performing independent online searches effectively. However, because the findings were based on intermittent opportunistic observations in a specific clinical setting, they may not be generalizable. ",
doi="10.2196/30687",
url="https://formative.jmir.org/2022/4/e30687",
url="http://www.ncbi.nlm.nih.gov/pubmed/35384844"
}


@Article{info:doi/10.2196/30258,
author="Douze, Laura
and Pelayo, Sylvia
and Messaadi, Nassir
and Grosjean, Julien
and Kerdelhu{\'e}, Ga{\'e}tan
and Marcilly, Romaric",
title="Designing Formulae for Ranking Search Results: Mixed Methods Evaluation Study",
journal="JMIR Hum Factors",
year="2022",
month="Mar",
day="25",
volume="9",
number="1",
pages="e30258",
keywords="information retrieval",
keywords="search engine",
keywords="topical relevance",
keywords="search result ranking",
keywords="user testing",
keywords="human factors",
abstract="Background: A major factor in the success of any search engine is the relevance of the search results; a tool should sort the search results to present the most relevant documents first. Assessing the performance of the ranking formula is an important part of search engine evaluation. However, the methods currently used to evaluate ranking formulae mainly collect quantitative data and do not gather qualitative data, which help to understand what needs to be improved to tailor the formulae to their end users. Objective: This study aims to evaluate 2 different parameter settings of the ranking formula of LiSSa (the French acronym for scientific literature in health care; Department of Medical Informatics and Information), a tool that provides access to health scientific literature in French, to adapt the formula to the needs of the end users. Methods: To collect quantitative and qualitative data, user tests were carried out with representative end users of LiSSa: 10 general practitioners and 10 registrars. Participants first assessed the relevance of the search results and then rated the ranking criteria used in the 2 formulae. Verbalizations were analyzed to characterize each criterion. Results: A formula that prioritized articles representing a consensus in the field was preferred. When users assess an article's relevance, they judge its topic, methods, and value in clinical practice. Conclusions: Following the evaluation, several improvements were implemented to give more weight to articles that match the search topic and to downgrade articles that have less informative or scientific value for the reader. Applying a qualitative methodology generates valuable user inputs to improve the ranking formula and move toward a highly usable search engine. ",
doi="10.2196/30258",
url="https://humanfactors.jmir.org/2022/1/e30258",
url="http://www.ncbi.nlm.nih.gov/pubmed/35333180"
}


@Article{info:doi/10.2196/31820,
author="Man, Alice
and van Ballegooie, Courtney",
title="Assessment of the Readability of Web-Based Patient Education Material From Major Canadian Pediatric Associations: Cross-sectional Study",
journal="JMIR Pediatr Parent",
year="2022",
month="Mar",
day="16",
volume="5",
number="1",
pages="e31820",
keywords="health literacy",
keywords="accessibility",
keywords="online health information",
keywords="pediatrics",
keywords="patient education",
abstract="Background: Web-based patient education materials (PEMs) are frequently written above the recommended reading level in North America. Poor PEM readability limits the accessibility of medical information for individuals with average literacy levels or lower. Pediatric hospital and association websites have not only been shown to be a preferred source of information among caregivers but have also become a necessity during the COVID-19 pandemic. The readability of Canadian pediatric association websites has not yet been assessed. Objective: The aim of this study is to determine if the content of PEMs from Canadian pediatric associations is written at a reading level that the majority of Canadians can understand. Methods: A total of 258 PEMs were extracted from 10 Canadian pediatric associations and evaluated for their reading level using 10 validated readability scales. The PEMs underwent a difficult word analysis and comparisons between PEMs from different associations were conducted. Results: Web-based PEMs were identified from 3 pediatric association websites, where the reading level (calculated as a grade level) was found to be an average of 8.8 (SD 1.8) for the Caring for Kids website, 9.5 (SD 2.2) for the Pediatric Endocrine Group website, and 13.1 (SD 2.1) for the Atlantic Pediatric Society website. The difficult word analysis identified that 19.9\% (SD 6.6\%) of words were unfamiliar, with 13.3\% (SD 5.3\%) and 31.9\% (SD 6.1\%) of words being considered complex (?3 syllables) and long (?6 letters), respectively. Conclusions: The web-based PEMs were found to be written above the recommended seventh-grade reading level for Canadians. Consideration should be made to create PEMs at an appropriate reading level for both patients and their caregivers to encourage health literacy and ultimately promote preventative health behaviors and improve child health outcomes. ",
doi="10.2196/31820",
url="https://pediatrics.jmir.org/2022/1/e31820",
url="http://www.ncbi.nlm.nih.gov/pubmed/35293875"
}


@Article{info:doi/10.2196/29819,
author="Okunoye, Babatunde
and Ning, Shaoyang
and Jemielniak, Dariusz",
title="Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends",
journal="JMIR Form Res",
year="2022",
month="Mar",
day="11",
volume="6",
number="3",
pages="e29819",
keywords="HIV/AIDS",
keywords="web search",
keywords="big data",
keywords="public health",
keywords="Wikipedia",
keywords="information seeking behavior",
keywords="online behavior",
keywords="online health information",
keywords="Google Trends",
abstract="Background: AIDS, caused by HIV, is a leading cause of mortality in Africa. HIV/AIDS is among the greatest public health challenges confronting health authorities, with South Africa having the greatest prevalence of the disease in the world. There is little research into how Africans meet their health information needs on HIV/AIDS online, and this research gap impacts programming and educational responses to the HIV/AIDS pandemic. Objective: This paper reports on how, in general, interest in the search terms ``HIV'' and ``AIDS'' mirrors the increase in people living with HIV and the decline in AIDS cases in South Africa. Methods: Data on search trends for HIV and AIDS for South Africa were found using the search terms ``HIV'' and ``AIDS'' (categories: health, web search) on Google Trends. This was compared with data on estimated adults and children living with HIV, and AIDS-related deaths in South Africa, from the Joint United Nations Programme on HIV/AIDS, and also with search interest in the topics ``HIV'' and ``AIDS'' on Wikipedia Afrikaans, the most developed local language Wikipedia service in South Africa. Nonparametric statistical tests were conducted to support the trends and associations identified in the data. Results: Google Trends shows a statistically significant decline (P<.001) in search interest for AIDS relative to HIV in South Africa. This trend mirrors progress on the ground in South Africa and is significantly associated (P<.001) with a decline in AIDS-related deaths and people living longer with HIV. This trend was also replicated on Wikipedia Afrikaans, where there was a greater interest in HIV than AIDS. Conclusions: This statistically significant (P<.001) association between interest in the search terms ``HIV'' and ``AIDS'' in South Africa (2004-2019) and the number of people living with HIV and AIDS in the country (2004-2019) might be an indicator that multilateral efforts at combating HIV/AIDS---particularly through awareness raising and behavioral interventions in South Africa---are bearing fruit, and this is not only evident on the ground, but is also reflected in the online information seeking on the HIV/AIDS pandemic. We acknowledge the limitation that in studying the association between Google search interests on HIV/AIDS and cases/deaths, causal relationships should not be drawn due to the limitations of the data. ",
doi="10.2196/29819",
url="https://formative.jmir.org/2022/3/e29819",
url="http://www.ncbi.nlm.nih.gov/pubmed/35275080"
}


@Article{info:doi/10.2196/32406,
author="El Sherif, Reem
and Pluye, Pierre
and Schuster, Tibor
and Grad, Roland",
title="Proxy Information Seeking by Users of a Parenting Information Website: Quantitative Observational Study",
journal="JMIR Pediatr Parent",
year="2022",
month="Mar",
day="4",
volume="5",
number="1",
pages="e32406",
keywords="consumer health information",
keywords="information seeking behavior",
keywords="child development",
keywords="child health",
keywords="information outcomes",
keywords="health information",
keywords="digital health",
keywords="parenting",
keywords="online information",
abstract="Background: One of the largest groups of consumers who seek health information on the internet are parents of young children, as well as people in their social circle. The concept of proxy seeking (on behalf of others) has been explored in the literature, yet little is known about the outcomes. Objective: The main aim of this study was to describe consumer health information outcomes reported by proxy seekers using a parenting website. Methods: We conducted a 2-year quantitative observational study. Participants were parents of 0- to 8-year-old children and members of their entourage in Canada who had accessed Na{\^i}tre et Grandir through the website or through a weekly newsletter. For each Na{\^i}tre et Grandir webpage, participants' perceptions regarding the outcomes of seeking and using specific webpages were gathered using a content-validated Information Assessment Method questionnaire. We compared the outcomes reported by parents with those reported by members of their entourage after consulting a parenting information website and explored if the method of accessing the information by the proxy seekers (website or weekly newsletter) changed the outcomes reported. For key primary survey items, the chi-square test was conducted, and differences in relative frequencies of responses were computed along with confidence intervals. Results: A total of 51,325 completed questionnaires were included in the analysis, pertaining to 1079 Na{\^i}tre et Grandir webpages (mean 48; range 1-637). Compared to parents, individuals in the entourage are more likely to report using the information in discussion with others (mean difference 0.166, 95\% CI 0.155-0.176). Parents, on the other hand, were more likely than the entourage to report using the information to better understand (mean difference 0.084, 95\% CI 0.073-0.094), to decide to do something (mean difference 0.156, 95\% CI 0.146-0.166), or to do something in a different manner (mean difference 0.052, 95\% CI 0.042-0.061). In addition, results suggest that the differences in perceived benefits of parenting information by the entourage depend on how they access the information. Respondents who were actively seeking the information (through the website) were more likely to report that the information would help them be less worried (mean difference 0.047; 95\% CI 0.024-0.069), handle a problem (mean difference 0.083; 95\% CI 0.062-0.104), and decide what to do with someone else (mean difference 0.040, 95\% CI 0.020-0.058). Respondents who passively acquired the information (through the newsletter) were more likely to report that the information would help improve the health or well-being of a child (mean difference 0.090; 95\% CI 0.067-0.112). Conclusions: By better understanding how consumers and their entourages use information, information providers can adapt information to meet both individual and group needs, and health care practitioners can target patients' entourages with web-based health information resources for dissemination and use. ",
doi="10.2196/32406",
url="https://pediatrics.jmir.org/2022/1/e32406",
url="http://www.ncbi.nlm.nih.gov/pubmed/35254283"
}


@Article{info:doi/10.2196/32235,
author="Lu, Yu
and Barrett, A. Laura
and Lin, Z. Rebecca
and Amith, Muhammad
and Tao, Cui
and He, Zhe",
title="Understanding Information Needs and Barriers to Accessing Health Information Across All Stages of Pregnancy: Systematic Review",
journal="JMIR Pediatr Parent",
year="2022",
month="Feb",
day="21",
volume="5",
number="1",
pages="e32235",
keywords="pregnancy",
keywords="information needs",
keywords="ontology",
keywords="systematic review",
keywords="fertility",
keywords="parenting",
keywords="pregnancy information",
keywords="online information",
keywords="health database",
abstract="Background: Understanding consumers' health information needs across all stages of the pregnancy trajectory is crucial to the development of mechanisms that allow them to retrieve high-quality, customized, and layperson-friendly health information. Objective: The objective of this study was to identify research gaps in pregnancy-related consumer information needs and available information from different sources. Methods: We conducted a systematic review of CINAHL, Cochrane, PubMed, and Web of Science for relevant articles that were published from 2009 to 2019. The quality of the included articles was assessed using the Critical Appraisal Skills Program. A descriptive data analysis was performed on these articles. Based on the review result, we developed the Pregnancy Information Needs Ontology (PINO) and made it publicly available in GitHub and BioPortal. Results: A total of 33 articles from 9 countries met the inclusion criteria for this review, of which the majority were published no earlier than 2016. Most studies were either descriptive (9/33, 27\%), interviews (7/33, 21\%), or surveys/questionnaires (7/33, 21\%); 20 articles mentioned consumers' pregnancy-related information needs. Half (9/18, 50\%) of the human-subject studies were conducted in the United States. More than a third (13/33, 39\%) of all studies focused on during-pregnancy stage; only one study (1/33, 3\%) was about all stages of pregnancy. The most frequent consumer information needs were related to labor delivery (9/20, 45\%), medication in pregnancy (6/20, 30\%), newborn care (5/20, 25\%), and lab tests (6/20, 30\%). The most frequently available source of information was the internet (15/24, 63\%). PINO consists of 267 classes, 555 axioms, and 271 subclass relationships. Conclusions: Only a few articles assessed the barriers to access to pregnancy-related information and the quality of each source of information; further work is needed. Future work is also needed to address the gaps between the information needed and the information available. ",
doi="10.2196/32235",
url="https://pediatrics.jmir.org/2022/1/e32235",
url="http://www.ncbi.nlm.nih.gov/pubmed/35188477"
}


@Article{info:doi/10.2196/29275,
author="Claggett, Jennifer
and Kitchens, Brent
and Paino, Maria
and Beisecker Levin, Kaitlyn",
title="The Effects of Website Traits and Medical Skepticism on Patients' Willingness to Follow Web-Based Medical Advice: Web-Based Experiment",
journal="J Med Internet Res",
year="2022",
month="Feb",
day="18",
volume="24",
number="2",
pages="e29275",
keywords="web-based information credibility assessment",
keywords="website traits",
keywords="medical skepticism",
keywords="mobile phone",
abstract="Background: As people increasingly turn to web-based sources for medical information, we offer some insight into what website traits influence patients' credibility assessment. Specifically, we control for brand and content length, while manipulating three website traits: authorship, format, and tone. Furthermore, we focus on medical skepticism to understand how patients with high levels of medical skepticism may react to web-based medical information differently. Medical skepticism is related to a patient's doubts about the value of conventional medical care; therefore, skeptics may have different practices and criteria when conducting their own web-based medical searches. Objective: The aim of this study is to evaluate how website traits affect the likelihood that patients follow web-based medical advice and how this varies among patients with differing levels of medical skepticism. Methods: This web-based experiment presented participants with a hypothetical medical situation about leg cramps and offered a website with treatment advice. We varied the websites the participants observed across three traits: authorship (patient or physician), format (article or discussion forum), and tone (objective or experience-based). The 2305 participants were randomly assigned to 1 of 8 possible conditions and then asked the extent to which they would follow the advice. Health care patterns and coverage, demographics, and the participants' level of medical skepticism were captured. Results: Our participants were selected to be demographically representative of the population of internet users in the United States. The 2305 complete responses were analyzed with ordinary least squares regression. Our analysis reveals that people are more likely to accept web-based medical advice authored by a physician (P<.001) and presented with an objective tone (P=.006), but these preferences erode as the levels of medical skepticism increase. Medical skepticism was measured by means of a previously established index on a 0 to 4 scale, and the average score was 2.26 (SD 0.84). Individuals with higher levels of medical skepticism were more likely to follow web-based medical advice in our experiment (P<.001). Individuals with low levels of medical skepticism found the discussion forum format more credible, whereas those with high levels of medical skepticism preferred the article format (P=.03). We discuss the interactions between medical skepticism and all 3 website traits manipulated in the experiment. Conclusions: Our findings suggest that, generally, physician authorship and an objective tone create more persuasive web-based medical advice. However, there are differences in how patients with high levels of medical skepticism react to web-based medical resources. Medical skeptics are less discerning regarding the author's credentials and the presentation tone of the information. Furthermore, patients with higher levels of medical skepticism prefer article format presentations, whereas those with lower levels of medical skepticism prefer discussion forum--style formatting. ",
doi="10.2196/29275",
url="https://www.jmir.org/2022/2/e29275",
url="http://www.ncbi.nlm.nih.gov/pubmed/35179506"
}


@Article{info:doi/10.2196/33651,
author="Fyhr, AnnSofie
and Persson, Johanna
and Ek, {\AA}sa",
title="Usage and Usability of a National e-Library for Chemotherapy Regimens: Mixed Methods Study",
journal="JMIR Hum Factors",
year="2022",
month="Feb",
day="17",
volume="9",
number="1",
pages="e33651",
keywords="chemotherapy regimens",
keywords="user evaluation",
keywords="standardization",
keywords="patient safety",
keywords="chemotherapy",
keywords="safety",
keywords="usability",
keywords="e-library",
keywords="medication errors",
abstract="Background: Accurate information about chemotherapy drugs and regimens is needed to reduce chemotherapy errors. A national e-library, as a common knowledge source with standardized chemotherapy nomenclature and content, was developed. Since the information in the library is both complex and extensive, it is central that the users can use the resource as intended. Objective: The aim of this study was to evaluate the usage and usability of an extensive e-library for chemotherapy regimens developed to reduce medication errors, support the health care staff in their work, and increase patient safety. Methods: To obtain a comprehensive evaluation, a mixed methods study was performed for a broad view of the usage, including a compilation of subjective views of the users (web survey, spontaneous user feedback, and qualitative interviews), analysis of statistics from the website, and an expert evaluation of the usability of the webpage. Results: Statistics from the website show an average of just over 2500 visits and 870 unique visitors per month. Most visits took place Mondays to Fridays, but there were 5-10 visits per day on weekends. The web survey, with 292 answers, shows that the visitors were mainly physicians and nurses. Almost 80\% (224/292) of respondents searched for regimens and 90\% (264/292) found what they were looking for and were satisfied with their visit. The expert evaluation shows that the e-library follows many existing design principles, thus providing some useful improvement suggestions. A total of 86 emails were received in 2020 with user feedback, most of which were from nurses. The main part (78\%, 67/86) contained a question, and the rest had discovered errors mainly in some regimen. The interviews reveal that most hospitals use a computerized physician order entry system, and they use the e-library in various ways, import XML files, transfer information, or use it as a reference. One hospital without a system uses the administration schedules from the library. Conclusions: The user evaluation indicates that the e-library is used in the intended manner and that the users can interact without problems. Users have different needs depending on their profession and their workplace, and these can be supported. The combination of methods applied ensures that the design and content comply with the users' needs and serves as feedback for continuous design and learning. With a broad national usage, the e-library can become a source for organizational and national learning and a source for continuous improvement of cancer care in Sweden. ",
doi="10.2196/33651",
url="https://humanfactors.jmir.org/2022/1/e33651",
url="http://www.ncbi.nlm.nih.gov/pubmed/35175199"
}


@Article{info:doi/10.2196/28252,
author="Marsall, Matthias
and Engelmann, Gerrit
and Skoda, Eva-Maria
and Teufel, Martin
and B{\"a}uerle, Alexander",
title="Measuring Electronic Health Literacy: Development, Validation, and Test of Measurement Invariance of a Revised German Version of the eHealth Literacy Scale",
journal="J Med Internet Res",
year="2022",
month="Feb",
day="2",
volume="24",
number="2",
pages="e28252",
keywords="eHealth",
keywords="eHeals",
keywords="health literacy",
keywords="factor analysis",
keywords="validation",
keywords="measurement invariance",
keywords="internet",
keywords="health information",
abstract="Background: The World Wide Web has become an essential source of health information. Nevertheless, the amount and quality of information provided may lead to information overload. Therefore, people need certain skills to search for, identify, and evaluate information from the internet. In the context of health information, these competencies are summarized as the construct of eHealth literacy. Previous research has highlighted the relevance of eHealth literacy in terms of health-related outcomes. However, the existing instrument assessing eHealth literacy in the German language reveals methodological limitations regarding test development and validation. The development and validation of a revised scale for this important construct is highly relevant. Objective: The objective of this study was the development and validation of a revised German eHealth literacy scale. In particular, this study aimed to focus on high methodological and psychometric standards to provide a valid and reliable instrument for measuring eHealth literacy in the German language. Methods: Two internationally validated instruments were merged to cover a wide scope of the construct of eHealth literacy and create a revised eHealth literacy scale. Translation into the German language followed scientific guidelines and recommendations to ensure content validity. Data from German-speaking people (n=470) were collected in a convenience sample from October to November 2020. Validation was performed by factor analyses. Further, correlations were performed to examine convergent, discriminant, and criterion validity. Additionally, analyses of measurement invariance of gender, age, and educational level were conducted. Results: Analyses revealed a 2-factorial model of eHealth literacy. By item-reduction, the 2 factors information seeking and information appraisal were measured with 8 items reaching acceptable-to-good model fits (comparative fit index [CFI]: 0.942, Tucker Lewis index [TLI]: 0.915, root mean square error of approximation [RMSEA]: 0.127, and standardized root mean square residual [SRMR]: 0.055). Convergent validity was comprehensively confirmed by significant correlations of information seeking and information appraisal with health literacy, internet confidence, and internet anxiety. Discriminant and criterion validity were examined by correlation analyses with various scales and could partly be confirmed. Scalar level of measurement invariance for gender (CFI: 0.932, TLI: 0.923, RMSEA: 0.122, and SRMR: 0.068) and educational level (CFI: 0.937, TLI: 0.934, RMSEA: 0.112, and SRMR: 0.063) were confirmed. Measurement invariance of age was rejected. Conclusions: Following scientific guidelines for translation and test validation, we developed a revised German eHealth Literacy Scale (GR-eHEALS). Our factor analyses confirmed an acceptable-to-good model fit. Construct validation in terms of convergent, discriminant, and criterion validity could mainly be confirmed. Our findings provide evidence for measurement invariance of the instrument regarding gender and educational level. The newly revised GR-eHEALS questionnaire represents a valid instrument to measure the important health-related construct eHealth literacy. ",
doi="10.2196/28252",
url="https://www.jmir.org/2022/2/e28252",
url="http://www.ncbi.nlm.nih.gov/pubmed/35107437"
}


@Article{info:doi/10.2196/26308,
author="Wang, Zhuoxin
and Fan, Yanyan
and Lv, Hekai
and Deng, Shanshan
and Xie, Hui
and Zhang, Li
and Luo, Aijing
and Wang, Fuzhi",
title="The Gap Between Self-Rated Health Information Literacy and Internet Health Information-Seeking Ability for Patients With Chronic Diseases in Rural Communities: Cross-sectional Study",
journal="J Med Internet Res",
year="2022",
month="Jan",
day="31",
volume="24",
number="1",
pages="e26308",
keywords="online",
keywords="health information",
keywords="barriers to acquisition",
keywords="middle-aged patients with chronic diseases",
keywords="rural community",
keywords="chronic conditions",
keywords="chronic",
keywords="rural",
keywords="literacy",
keywords="information seeking",
abstract="Background: The internet has become one of the most important channels for residents to seek health information, particularly in remote rural areas in China. Objective: In this study, we aimed to explore the gap between self-rated health information literacy and internet health information seeking ability for patients with chronic diseases in rural communities and to preliminarily evaluate their barriers when seeking health information via the internet. Methods: Residents from rural communities near Bengbu City and with chronic diseases were included in this study. A self-rated questionnaire was used to evaluate their health information literacy, 3 behavioral competency tasks were designed to preliminarily evaluate their ability to seek health information on the internet and semistructured interviews were used to investigate their barriers to obtaining health information via the internet. A small audiorecorder was used to record the interview content, and screen-recording software was used to record the participants' behavior during the web-based operational tasks. Results: A total of 70 respondents completed the self-rated health information literacy questionnaire and the behavioral competence test, and 56 respondents participated in the semistructured interviews. Self-rated health information literacy (score out of 70: mean 46.21, SD 4.90) of the 70 respondents were moderate. Although 91\% (64/70) of the respondents could find health websites, and 93\% (65/70) of the respondents could find information on treatment that they thought was the best, 35\% (23/65) of respondents did not know how to save the results they had found. The operational tasks indicated that most articles selected by the respondents came from websites with encyclopedic knowledge or answers from people based on their own experiences rather than authoritative health information websites. After combining the results of the semistructured interviews with the DISCERN scale test results, we found that most interviewees had difficulty obtaining high-quality health information via the internet. Conclusions: Although the health information literacy level of patients with rural chronic disease was moderate, they lack the ability to access high-quality health information via the internet. The vast majority of respondents recognized the importance of accessing health information but were not very proactive in accessing such information. ",
doi="10.2196/26308",
url="https://www.jmir.org/2022/1/e26308",
url="http://www.ncbi.nlm.nih.gov/pubmed/35099401"
}


@Article{info:doi/10.2196/30679,
author="Dashtian, Hassan
and Murthy, Dhiraj
and Kong, Grace",
title="An Exploration of e-Cigarette--Related Search Items on YouTube: Network Analysis",
journal="J Med Internet Res",
year="2022",
month="Jan",
day="27",
volume="24",
number="1",
pages="e30679",
keywords="electronic nicotine delivery systems",
keywords="vaping",
keywords="social media",
keywords="search engine",
keywords="natural language processing",
keywords="social network analysis",
abstract="Background: e-Cigarette use among youth is high, which may be due in part to pro--e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette--related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette--related search items and results. Objective: The aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette--related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube. Methods: We used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network. Results: By modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34\%-57\%), cannabis vaping (16\%-28\%), e-liquid (14\%-37\%), and other (8\%-22\%). Conclusions: Our findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette--related search items but also help determine which search items are more likely to act as a gateway to e-cigarette--related content. ",
doi="10.2196/30679",
url="https://www.jmir.org/2022/1/e30679",
url="http://www.ncbi.nlm.nih.gov/pubmed/35084353"
}


@Article{info:doi/10.2196/31063,
author="Siegersma, R. Klaske
and Evers, Maxime
and Bots, H. Sophie
and Groepenhoff, Floor
and Appelman, Yolande
and Hofstra, Leonard
and Tulevski, I. Igor
and Somsen, Aernout G.
and den Ruijter, M. Hester
and Spruit, Marco
and Onland-Moret, Charlotte N.",
title="Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching",
journal="JMIR Med Inform",
year="2022",
month="Jan",
day="25",
volume="10",
number="1",
pages="e31063",
keywords="adverse drug reactions",
keywords="word embeddings",
keywords="clinical notes",
abstract="Background: Knowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering accurate information that can be retrieved from clinical notes about the incidence of ADRs is of great relevance. However, manual labeling of these notes is time-consuming, and automatization can improve the use of free-text clinical notes for the identification of ADRs. Furthermore, tools for language processing in languages other than English are not widely available. Objective: The aim of this study is to design and evaluate a method for automatic extraction of medication and Adverse Drug Reaction Identification in Clinical Notes (ADRIN). Methods: Dutch free-text clinical notes (N=277,398) and medication registrations (N=499,435) from the Cardiology Centers of the Netherlands database were used. All clinical notes were used to develop word embedding models. Vector representations of word embedding models and string matching with a medical dictionary (Medical Dictionary for Regulatory Activities [MedDRA]) were used for identification of ADRs and medication in a test set of clinical notes that were manually labeled. Several settings, including search area and punctuation, could be adjusted in the prototype to evaluate the optimal version of the prototype. Results: The ADRIN method was evaluated using a test set of 988 clinical notes written on the stop date of a drug. Multiple versions of the prototype were evaluated for a variety of tasks. Binary classification of ADR presence achieved the highest accuracy of 0.84. Reduced search area and inclusion of punctuation improved performance, whereas incorporation of the MedDRA did not improve the performance of the pipeline. Conclusions: The ADRIN method and prototype are effective in recognizing ADRs in Dutch clinical notes from cardiac diagnostic screening centers. Surprisingly, incorporation of the MedDRA did not result in improved identification on top of word embedding models. The implementation of the ADRIN tool may help increase the identification of ADRs, resulting in better care and saving substantial health care costs. ",
doi="10.2196/31063",
url="https://medinform.jmir.org/2022/1/e31063",
url="http://www.ncbi.nlm.nih.gov/pubmed/35076407"
}


@Article{info:doi/10.2196/27434,
author="Ahne, Adrian
and Fagherazzi, Guy
and Tannier, Xavier
and Czernichow, Thomas
and Orchard, Francisco",
title="Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study",
journal="J Med Internet Res",
year="2022",
month="Jan",
day="18",
volume="24",
number="1",
pages="e27434",
keywords="evidence-based medicine",
keywords="clinical decision making",
keywords="clinical decision support",
keywords="digital health",
keywords="medical informatics",
keywords="transparency",
keywords="hierarchical clustering",
keywords="active learning",
keywords="classification",
keywords="memory consumption",
keywords="natural language processing",
abstract="Background: The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty. Objective: We developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed. Methods: The methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs). Results: For the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents. Conclusions: We proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process. ",
doi="10.2196/27434",
url="https://www.jmir.org/2022/1/e27434",
url="http://www.ncbi.nlm.nih.gov/pubmed/35040795"
}


@Article{info:doi/10.2196/29153,
author="Harden, M. Samantha
and Murphy, Anna
and Ratliff, Kathryn
and Balis, E. Laura",
title="Internet Search Results for Older Adult Physical Activity Guidelines: Scoping Review",
journal="JMIR Form Res",
year="2022",
month="Jan",
day="13",
volume="6",
number="1",
pages="e29153",
keywords="dissemination",
keywords="information seeking",
keywords="health communication",
keywords="elderly",
abstract="Background: Older adults seek health-related information through casual internet searches. Yet, researchers focus on peer-reviewed journals and conference presentations as primary dissemination strategies. Representatives of mass media are alerted (passive diffusion) of new studies or recommendations, but the veracity of the information shared is not often analyzed, and when it is, the analysis is often not comprehensive. However, most older adults do not have access to peer-reviewed journal articles or paid subscription services for more reputable media outlets. Objective: We aimed to determine what information was readily available (ie, open access) to older adults who may casually search the internet for physical activity recommendations. Methods: We performed a 6-part scoping review to determine the research question and available evidence, and extract data within open-access top hits using popular online search engines. Results were categorized by a dissemination model that has categories of sources, channels, audience, and messages. Results: After the iterative search process, 92 unique articles were included and coded. Only 5 (5\%) cited physical activity guidelines, and most were coded as promoting healthy aging (82/92, 89\%) and positive framing (84/92, 91\%). Most articles were posed as educational, but the authors' credentials were rarely reported (ie, 22\% of the time). Muscle strengthening and balance components of the physical activity guidelines for older adults were rarely reported (72/92, 78\% and 80/92, 87\%, respectively) or inaccurately reported (3/92, 3\% and 3/92, 3\%, respectively). Conclusions: Inconsistent messages lead to mistrust of science and public health representatives. This work highlights the lack of evidence within existing open-access resources. Further efforts are needed to ensure evidence-based public health messages are in the sources and channels older adults are using to inform their knowledge and behaviors. ",
doi="10.2196/29153",
url="https://formative.jmir.org/2022/1/e29153",
url="http://www.ncbi.nlm.nih.gov/pubmed/35023847"
}


@Article{info:doi/10.2196/25440,
author="Ulrich, Hannes
and Kock-Schoppenhauer, Ann-Kristin
and Deppenwiese, Noemi
and G{\"o}tt, Robert
and Kern, Jori
and Lablans, Martin
and Majeed, W. Raphael
and St{\"o}hr, R. Mark
and Stausberg, J{\"u}rgen
and Varghese, Julian
and Dugas, Martin
and Ingenerf, Josef",
title="Understanding the Nature of Metadata: Systematic Review",
journal="J Med Internet Res",
year="2022",
month="Jan",
day="11",
volume="24",
number="1",
pages="e25440",
keywords="metadata",
keywords="metadata definition",
keywords="systematic review",
keywords="data integration",
keywords="data identification",
keywords="data classification",
abstract="Background: Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term ``metadata'' and its use is not always unambiguous. Objective: This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse. Methods: A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used. Results: The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas. Conclusions: Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context. ",
doi="10.2196/25440",
url="https://www.jmir.org/2022/1/e25440",
url="http://www.ncbi.nlm.nih.gov/pubmed/35014967"
}


@Article{info:doi/10.2196/51984,
author="Silver, A. Reginald
and Johnson, Chandrika",
title="Health Information Seeking Behavior on Social Networking Sites and Self-Treatment: Pilot Survey Study",
journal="Online J Public Health Inform",
year="2023",
month="Dec",
day="20",
volume="15",
pages="e51984",
keywords="health care seeking behavior",
keywords="online social networking",
keywords="sociodemographic factors",
keywords="community survey",
keywords="logistic regression",
keywords="self-treatment",
abstract="Background: Social networking site use and social network--based health information seeking behavior have proliferated to the point that the lines between seeking health information from credible social network--based sources and the decision to seek medical care or attempt to treat oneself have become blurred. Objective: We contribute to emerging research on health information seeking behavior by investigating demographic factors, social media use for health information seeking purposes, and the relationship between health information seeking and occurrences of self-treatment. Methods: Data were collected from an online survey in which participants were asked to describe sociodemographic factors about themselves, social media use patterns, perceptions about their motivations for health information seeking on social media platforms, and whether or not they attempted self-treatment after their social media--related health information seeking. We conducted a binomial logistic regression with self-treatment as a dichotomous categorical dependent variable. Results: Results indicate that significant predictors of self-treatment based on information obtained from social networking sites include race, exercise frequency, and degree of trust in the health-related information received. Conclusions: With an understanding of how sociodemographic factors might influence the decision to self-treat based on information obtained from social networking sites, health care providers can assist patients by educating them on credible social network--based sources of health information and discussing the importance of seeking medical advice from a health care provider. ",
doi="10.2196/51984",
url="https://ojphi.jmir.org/2023/1/e51984",
url="http://www.ncbi.nlm.nih.gov/pubmed/38179207"
}


@Article{info:doi/10.2196/46929,
author="Cummerow, Julia
and Wienecke, Christin
and Engler, Nicola
and Marahrens, Philip
and Gruening, Philipp
and Steinh{\"a}user, Jost",
title="Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review",
journal="J Med Internet Res",
year="2023",
month="Dec",
day="14",
volume="25",
pages="e46929",
keywords="cough",
keywords="predictor",
keywords="differential diagnosis",
keywords="primary health care",
keywords="artificial intelligence",
abstract="Background: Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined. Objective: Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice. Methods: The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor. Results: In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31\%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent. Conclusions: Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning--based diagnostic algorithm in a meaningful way. ",
doi="10.2196/46929",
url="https://www.jmir.org/2023/1/e46929",
url="http://www.ncbi.nlm.nih.gov/pubmed/38096024"
}


@Article{info:doi/10.2196/47595,
author="Jiao, Wen
and Chang, Angela
and Ho, Mary
and Lu, Qianfeng
and Liu, Tingchi Matthew
and Schulz, Johannes Peter",
title="Predicting and Empowering Health for Generation Z by Comparing Health Information Seeking and Digital Health Literacy: Cross-Sectional Questionnaire Study",
journal="J Med Internet Res",
year="2023",
month="Oct",
day="30",
volume="25",
pages="e47595",
keywords="health information seeking",
keywords="digital health literacy",
keywords="health empowerment",
keywords="Generation Z",
keywords="digitally savvy",
abstract="Background: Generation Z (born 1995-2010) members are digital residents who use technology and the internet more frequently than any previous generation to learn about their health. They are increasingly moving away from conventional methods of seeking health information as technology advances quickly and becomes more widely available, resulting in a more digitalized health care system. Similar to all groups, Generation Z has specific health care requirements and preferences, and their use of technology influences how they look for health information. However, they have often been overlooked in scholarly research. Objective: First, we aimed to identify the information-seeking preferences of older individuals and Generation Z (those between the ages of 18 and 26 years); second, we aimed to predict the effects of digital health literacy and health empowerment in both groups. We also aimed to identify factors that impact how both groups engage in digital health and remain in control of their own health. Methods: The Health Information National Trends Survey was adopted for further use in 2022. We analyzed 1862 valid data points by conducting a survey among Chinese respondents to address the research gap. A descriptive analysis, 2-tailed t test, and multiple linear regression were applied to the results. Results: When compared with previous generations, Generation Z respondents (995/1862, 53.44\%) were more likely to use the internet to find out about health-related topics, whereas earlier generations relied more on traditional media and interpersonal contact. Web-based information-seeking behavior is predicted by digital health literacy (Generation Z: $\beta$=.192, P<.001; older population: $\beta$=.337, P<.001). While this was happening, only seeking health information from physicians positively predicted health empowerment (Generation Z: $\beta$=.070, P=.002; older population: $\beta$=.089, P<.001). Despite more frequent use of the internet to learn about their health, Generation Z showed lower levels of health empowerment and less desire to look for health information, overall. Conclusions: This study examined and compared the health information--seeking behaviors of Generation Z and older individuals to improve their digital health literacy and health empowerment. The 2 groups demonstrated distinct preferences regarding their choice of information sources. Health empowerment and digital health literacy were both significantly related to information-seeking behaviors. ",
doi="10.2196/47595",
url="https://www.jmir.org/2023/1/e47595",
url="http://www.ncbi.nlm.nih.gov/pubmed/37902832"
}


@Article{info:doi/10.2196/48143,
author="Engineer, Margi
and Kot, Sushant
and Dixon, Emma",
title="Investigating the Readability and Linguistic, Psychological, and Emotional Characteristics of Digital Dementia Information Written in the English Language: Multitrait-Multimethod Text Analysis",
journal="JMIR Form Res",
year="2023",
month="Oct",
day="25",
volume="7",
pages="e48143",
keywords="natural language processing",
keywords="consumer health information",
keywords="readability",
keywords="Alzheimer disease and related dementias",
keywords="caregivers",
abstract="Background: Past research in the Western context found that people with dementia search for digital dementia information in peer-reviewed medical research articles, dementia advocacy and medical organizations, and blogs written by other people with dementia. This past work also demonstrated that people with dementia do not perceive English digital dementia information as emotionally or cognitively accessible. Objective: In this study, we sought to investigate the readability; linguistic, psychological, and emotional characteristics; and target audiences of digital dementia information. We conducted a textual analysis of 3 different types of text-based digital dementia information written in English: 300 medical articles, 35 websites, and 50 blogs. Methods: We assessed the text's readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level measurements, as well as tone, analytical thinking, clout, authenticity, and word frequencies using a natural language processing tool, Linguistic Inquiry and Word Count Generator. We also conducted a thematic analysis to categorize the target audiences for each information source and used these categorizations for further statistical analysis. Results: The median Flesch-Kincaid Grade Level readability score and Flesch Reading Ease score for all types of information (N=1139) were 12.1 and 38.6, respectively, revealing that the readability scores of all 3 information types were higher than the minimum requirement. We found that medical articles had significantly (P=.05) higher word count and analytical thinking scores as well as significantly lower clout, authenticity, and emotional tone scores than websites and blogs. Further, blogs had significantly (P=.48) higher word count and authenticity scores but lower analytical scores than websites. Using thematic analysis, we found that most of the blogs (156/227, 68.7\%) and web pages (399/612, 65.2\%) were targeted at people with dementia. Website information targeted at a general audience had significantly lower readability scores. In addition, website information targeted at people with dementia had higher word count and lower emotional tone ratings. The information on websites targeted at caregivers had significantly higher clout and lower authenticity scores. Conclusions: Our findings indicate that there is an abundance of digital dementia information written in English that is targeted at people with dementia, but this information is not readable by a general audience. This is problematic considering that people with <12 years of education are at a higher risk of developing dementia. Further, our findings demonstrate that digital dementia information written in English has a negative tone, which may be a contributing factor to the mental health crisis many people with dementia face after receiving a diagnosis. Therefore, we call for content creators to lower readability scores to make the information more accessible to a general audience and to focus their efforts on providing information in a way that does not perpetuate overly negative narratives of dementia. ",
doi="10.2196/48143",
url="https://formative.jmir.org/2023/1/e48143",
url="http://www.ncbi.nlm.nih.gov/pubmed/37878351"
}


@Article{info:doi/10.2196/49901,
author="Lane, Hakan
and Walker, Mark",
title="The Impact of Temperature, Humidity, and Sunshine on Internet Search Volumes Related to Psoriasis",
journal="JMIR Dermatol",
year="2023",
month="Oct",
day="19",
volume="6",
pages="e49901",
keywords="psoriasis",
keywords="infodemiology",
keywords="internet search",
keywords="internet searching",
keywords="web search",
keywords="information seeking",
keywords="information search behavior",
keywords="information search behaviour",
keywords="dermatology",
keywords="skin",
keywords="weather",
keywords="temperature",
keywords="humidity",
keywords="sunshine",
doi="10.2196/49901",
url="https://derma.jmir.org/2023/1/e49901",
url="http://www.ncbi.nlm.nih.gov/pubmed/37856189"
}


@Article{info:doi/10.2196/48607,
author="Hui, Vivian
and Eby, Malavika
and Constantino, Eva Rose
and Lee, Heeyoung
and Zelazny, Jamie
and Chang, C. Judy
and He, Daqing
and Lee, Ji Young",
title="Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach",
journal="J Med Internet Res",
year="2023",
month="Oct",
day="9",
volume="25",
pages="e48607",
keywords="intimate partner violence",
keywords="text mining",
keywords="social media",
keywords="online health communities",
keywords="linguistic features",
abstract="Background: Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. Objective: The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. Methods: We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ?18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. Results: Overall, 250 postings were included, and 200 (80\%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. Conclusions: OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively. ",
doi="10.2196/48607",
url="https://www.jmir.org/2023/1/e48607",
url="http://www.ncbi.nlm.nih.gov/pubmed/37812467"
}


@Article{info:doi/10.2196/39736,
author="Orel, Erol
and Ciglenecki, Iza
and Thiabaud, Amaury
and Temerev, Alexander
and Calmy, Alexandra
and Keiser, Olivia
and Merzouki, Aziza",
title="An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study",
journal="J Med Internet Res",
year="2023",
month="Sep",
day="15",
volume="25",
pages="e39736",
keywords="LiteRev",
keywords="literature review",
keywords="natural language processing",
keywords="machine learning",
keywords="automation",
keywords="clustering",
keywords="topic",
keywords="acute",
keywords="early",
keywords="HIV",
abstract="Background: Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly. Objective: LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev. Methods: Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling. Results: LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5\% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6\%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5\%). This represents a total work saved over sampling of 56\%. Conclusions: We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest. ",
doi="10.2196/39736",
url="https://www.jmir.org/2023/1/e39736",
url="http://www.ncbi.nlm.nih.gov/pubmed/37713261"
}


@Article{info:doi/10.2196/48630,
author="Park, Albert
and Sayed, Fatima
and Robinson, Patrick
and Elopre, Latesha
and Ge, Yaorong
and Li, Shaoyu
and Grov, Christian
and Sullivan, Sean Patrick",
title="Health Information on Pre-Exposure Prophylaxis From Search Engines and Twitter: Readability Analysis",
journal="JMIR Public Health Surveill",
year="2023",
month="Sep",
day="4",
volume="9",
pages="e48630",
keywords="pre-exposure prophylaxis",
keywords="PrEP",
keywords="health literacy",
keywords="health education materials",
keywords="readability",
keywords="prophylaxis",
keywords="health information",
keywords="electronic health education",
keywords="HIV",
keywords="infection",
keywords="Twitter",
abstract="Background: Pre-exposure prophylaxis (PrEP) is proven to prevent HIV infection. However, PrEP uptake to date has been limited and inequitable. Analyzing the readability of existing PrEP-related information is important to understand the potential impact of available PrEP information on PrEP uptake and identify opportunities to improve PrEP-related education and communication. Objective: We examined the readability of web-based PrEP information identified using search engines and on Twitter. We investigated the readability of web-based PrEP documents, stratified by how the PrEP document was obtained on the web, information source, document format and communication method, PrEP modality, and intended audience. Methods: Web-based PrEP information in English was systematically identified using search engines and the Twitter API. We manually verified and categorized results and described the method used to obtain information, information source, document format and communication method, PrEP modality, and intended audience. Documents were converted to plain text for the analysis and readability of the collected documents was assessed using 4 readability indices. We conducted pairwise comparisons of readability based on how the PrEP document was obtained on the web, information source, document format, communication method, PrEP modality, and intended audience, then adjusted for multiple comparisons. Results: A total of 463 documents were identified. Overall, the readability of web-based PrEP information was at a higher level (10.2-grade reading level) than what is recommended for health information provided to the general public (ninth-grade reading level, as suggested by the Department of Health and Human Services). Brochures (n=33, 7\% of all identified resources) were the only type of PrEP materials that achieved the target of ninth-grade reading level. Conclusions: Web-based PrEP information is often written at a complex level for potential and current PrEP users to understand. This may hinder PrEP uptake for some people who would benefit from it. The readability of PrEP-related information found on the web should be improved to align more closely with health communication guidelines for reading level to improve access to this important health information, facilitate informed decisions by those with a need for PrEP, and realize national prevention goals for PrEP uptake and reducing new HIV infections in the United States. ",
doi="10.2196/48630",
url="https://publichealth.jmir.org/2023/1/e48630",
url="http://www.ncbi.nlm.nih.gov/pubmed/37665621"
}


@Article{info:doi/10.2196/46275,
author="Xu, Ting
and Ma, Yuming
and Pan, Tianya
and Chen, Yifei
and Liu, Yuhua
and Zhu, Fudong
and Zhou, Zhiguang
and Chen, Qianming",
title="Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study",
journal="JMIR Med Inform",
year="2023",
month="Aug",
day="1",
volume="11",
pages="e46275",
keywords="visual analytics",
keywords="oral health data mining",
keywords="knowledge graph",
keywords="multidimensional data visualization",
abstract="Background: Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. Objective: We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. Methods: First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. Results: A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. Conclusions: A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases. ",
doi="10.2196/46275",
url="https://medinform.jmir.org/2023/1/e46275",
url="http://www.ncbi.nlm.nih.gov/pubmed/37526971"
}


@Article{info:doi/10.2196/41858,
author="Huang, Shih-Tsung
and Hsiao, Fei-Yuan
and Tsai, Tsung-Hsien
and Chen, Pei-Jung
and Peng, Li-Ning
and Chen, Liang-Kung",
title="Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study",
journal="J Med Internet Res",
year="2023",
month="Jul",
day="26",
volume="25",
pages="e41858",
keywords="dementia",
keywords="machine learning",
keywords="cluster analysis",
keywords="disease",
keywords="condition",
keywords="symptoms",
keywords="data",
keywords="data set",
keywords="cardiovascular",
keywords="neuropsychiatric",
keywords="infection",
keywords="mobility",
keywords="mental conditions",
keywords="development",
abstract="Background: Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. Objective: This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. Methods: Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67\% for the training data set and n=5234, 33\% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. Results: Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. Conclusions: Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world. ",
doi="10.2196/41858",
url="https://www.jmir.org/2023/1/e41858",
url="http://www.ncbi.nlm.nih.gov/pubmed/37494081"
}


@Article{info:doi/10.2196/45059,
author="Agnello, Marie Danielle
and Loisel, Armand Quentin Emile
and An, Qingfan
and Balaskas, George
and Chrifou, Rabab
and Dall, Philippa
and de Boer, Janneke
and Delfmann, Rahel Lea
and Gin{\'e}-Garriga, Maria
and Goh, Kunshan
and Longworth, Raffaella Giuliana
and Messiha, Katrina
and McCaffrey, Lauren
and Smith, Niamh
and Steiner, Artur
and Vogelsang, Mira
and Chastin, Sebastien",
title="Establishing a Health CASCADE--Curated Open-Access Database to Consolidate Knowledge About Co-Creation: Novel Artificial Intelligence--Assisted Methodology Based on Systematic Reviews",
journal="J Med Internet Res",
year="2023",
month="Jul",
day="18",
volume="25",
pages="e45059",
keywords="co-creation",
keywords="co-production",
keywords="co-design",
keywords="database",
keywords="participatory",
keywords="methodology",
keywords="artificial intelligence",
abstract="Background: Co-creation is an approach that aims to democratize research and bridge the gap between research and practice, but the potential fragmentation of knowledge about co-creation has hindered progress. A comprehensive database of published literature from multidisciplinary sources can address this fragmentation through the integration of diverse perspectives, identification and dissemination of best practices, and increase clarity about co-creation. However, two considerable challenges exist. First, there is uncertainty about co-creation terminology, making it difficult to identify relevant literature. Second, the exponential growth of scientific publications has led to an overwhelming amount of literature that surpasses the human capacity for a comprehensive review. These challenges hinder progress in co-creation research and underscore the need for a novel methodology to consolidate and investigate the literature. Objective: This study aimed to synthesize knowledge about co-creation across various fields through the development and application of an artificial intelligence (AI)--assisted selection process. The ultimate goal of this database was to provide stakeholders interested in co-creation with relevant literature. Methods: We created a novel methodology for establishing a curated database. To accommodate the variation in terminology, we used a broad definition of co-creation that encompassed the essence of existing definitions. To filter out irrelevant information, an AI-assisted selection process was used. In addition, we conducted bibliometric analyses and quality control procedures to assess content and accuracy. Overall, this approach allowed us to develop a robust and reliable database that serves as a valuable resource for stakeholders interested in co-creation. Results: The final version of the database included 13,501 papers, which are indexed in Zenodo and accessible in an open-access downloadable format. The quality assessment revealed that 20.3\% (140/688) of the database likely contained irrelevant material, whereas the methodology captured 91\% (58/64) of the relevant literature. Participatory and variations of the term co-creation were the most frequent terms in the title and abstracts of included literature. The predominant source journals included health sciences, sustainability, environmental sciences, medical research, and health services research. Conclusions: This study produced a high-quality, open-access database about co-creation. The study demonstrates that it is possible to perform a systematic review selection process on a fragmented concept using human-AI collaboration. Our unified concept of co-creation includes the co-approaches (co-creation, co-design, and co-production), forms of participatory research, and user involvement. Our analysis of authorship, citations, and source landscape highlights the potential lack of collaboration among co-creation researchers and underscores the need for future investigation into the different research methodologies. The database provides a resource for relevant literature and can support rapid literature reviews about co-creation. It also offers clarity about the current co-creation landscape and helps to address barriers that researchers may face when seeking evidence about co-creation. ",
doi="10.2196/45059",
url="https://www.jmir.org/2023/1/e45059",
url="http://www.ncbi.nlm.nih.gov/pubmed/37463024"
}


@Article{info:doi/10.2196/41806,
author="Darien, Kaja
and Lee, Susan
and Knowles, Kayla
and Wood, Sarah
and Langer, D. Miriam
and Lazar, Nellie
and Dowshen, Nadia",
title="Health Information From Web Search Engines and Virtual Assistants About Pre-Exposure Prophylaxis for HIV Prevention in Adolescents and Young Adults: Content Analysis",
journal="JMIR Pediatr Parent",
year="2023",
month="Jul",
day="18",
volume="6",
pages="e41806",
keywords="pre-exposure prophylaxis",
keywords="PrEP",
keywords="prophylaxis",
keywords="internet use",
keywords="search engine",
keywords="adolescent",
keywords="youth",
keywords="pediatric",
keywords="adolescence",
keywords="young adult",
keywords="readability",
keywords="human immunodeficiency virus",
keywords="HIV",
keywords="virtual assistant",
keywords="health information",
keywords="information quality",
keywords="accuracy",
keywords="credibility",
keywords="patient education",
keywords="comprehension",
keywords="comprehensible",
keywords="web-based",
keywords="online information",
keywords="sexual health",
keywords="reading level",
abstract="Background: Adolescents and young adults are disproportionately affected by HIV, suggesting that HIV prevention methods such as pre-exposure prophylaxis (PrEP) should focus on this group as a priority. As digital natives, youth likely turn to internet resources regarding health topics they may not feel comfortable discussing with their medical providers. To optimize informed decision-making by adolescents and young adults most impacted by HIV, the information from internet searches should be educational, accurate, and readable. Objective: The aims of this study were to compare the accuracy of web-based PrEP information found using web search engines and virtual assistants, and to assess the readability of the resulting information. Methods: Adolescent HIV prevention clinical experts developed a list of 23 prevention-related questions that were posed to search engines (Ask.com, Bing, Google, and Yahoo) and virtual assistants (Amazon Alexa, Microsoft Cortana, Google Assistant, and Apple Siri). The first three results from search engines and virtual assistant web references, as well as virtual assistant verbal responses, were recorded and coded using a six-tier scale to assess the quality of information produced. The results were also entered in a web-based tool determining readability using the Flesch-Kincaid Grade Level scale. Results: Google web search engine and Google Assistant more frequently produced PrEP information of higher quality than the other search engines and virtual assistants with scores ranging from 3.4 to 3.7 and 2.8 to 3.3, respectively. Additionally, the resulting information generally was presented in language at a seventh and 10th grade reading level according to the Flesch-Kincaid Grade Level scale. Conclusions: Adolescents and young adults are large consumers of technology and may experience discomfort discussing their sexual health with providers. It is important that efforts are made to ensure the information they receive about HIV prevention methods, and PrEP in particular, is comprehensive, comprehensible, and widely available. ",
doi="10.2196/41806",
url="https://pediatrics.jmir.org/2023/1/e41806",
url="http://www.ncbi.nlm.nih.gov/pubmed/37463044"
}


@Article{info:doi/10.2196/45550,
author="O'Neill, Braden
and Ferguson, Jacob
and Dalueg, Lauren
and Yusuf, Abban
and Kirubarajan, Abirami
and Lloyd, Taryn
and Mollanji, Eisi
and Persaud, Navindra",
title="Evaluating the Supporting Evidence of Medical Cannabis Claims Made on Clinic Websites: Cross-Sectional Study",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="29",
volume="25",
pages="e45550",
keywords="cannabis",
keywords="evidence-based medicine",
keywords="adverse effects",
keywords="consumer health information",
abstract="Background: Since the legalization of medical cannabis in Canada in 2013, prescription of cannabis for medical purposes has become commonplace and a multibillion dollar industry has formed. Much of the media coverage surrounding medical cannabis has been positive in nature, leading to Canadians potentially underestimating the adverse effects of medical cannabis use. In recent years, there has been a large increase in clinic websites advertising the use of medical cannabis for health indications. However, little is known about the quality of the evidence used by these clinic websites to describe the effectiveness of cannabis used for medical purposes. Objective: We aimed to identify the indications for medical cannabis reported by cannabis clinics in Ontario, Canada, and the evidence these clinics cited to support cannabis prescription. Methods: We conducted a cross-sectional web search to identify all cannabis clinic websites within Ontario, Canada, that had physician involvement and identified their primary purpose as cannabis prescription. Two reviewers independently searched these websites to identify all medical indications for which cannabis was promoted and reviewed and critically appraised all studies cited using the Oxford Centre for Evidence-Based Medicine Levels of Evidence rubric. Results: A total of 29 clinics were identified, promoting cannabis for 20 different medical indications including migraines, insomnia, and fibromyalgia. There were 235 unique studies cited on these websites to support the effectiveness of cannabis for these indications. A high proportion (36/235, 15.3\%) of the studies were identified to be at the lowest level of evidence (level 5). Only 4 clinic websites included any mention of harms associated with cannabis. Conclusions: Cannabis clinic websites generally promote cannabis use as medically effective but cite low-quality evidence to support these claims and rarely discuss harms. The recommendation of cannabis as a general therapeutic for many indications unsupported by high-quality evidence is potentially misleading for medical practitioners and patients. This disparity should be carefully evaluated in context of the specific medical indication and an individualized patient risk assessment. Our work illustrates the need to increase the quality of research performed on the medical effects of cannabis. ",
doi="10.2196/45550",
url="https://www.jmir.org/2023/1/e45550",
url="http://www.ncbi.nlm.nih.gov/pubmed/37384372"
}


@Article{info:doi/10.2196/41997,
author="Weber, Matthew
and Armour, L. Veronica
and Lindstadt, Calandra
and Yanovitzky, Itzhak",
title="Testing Multiple Methods to Effectively Promote Use of a Knowledge Portal to Health Policy Makers: Quasi-Experimental Evaluation",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="28",
volume="25",
pages="e41997",
keywords="depression",
keywords="depression screening",
keywords="policy making",
keywords="Google Ads",
keywords="analytics",
keywords="knowledge brokers",
keywords="knowledge sharing",
keywords="online",
keywords="resources",
keywords="teen",
keywords="young adult",
keywords="effectiveness",
abstract="Background: Health policy makers and advocates increasingly utilize online resources for policy-relevant knowledge. Knowledge brokering is one potential mechanism to encourage the use of research evidence in policy making, but the mechanisms of knowledge brokerage in online spaces are understudied. This work looks at knowledge brokerage through the launch of Project ASPEN, an online knowledge portal developed in response to a New Jersey legislative act that established a pilot program for adolescent depression screening for young adults in grades 7-12. Objective: This study compares the ability to drive policy brief downloads by policy makers and advocates from the Project ASPEN knowledge portal using a variety of online methods to promote the knowledge portal. Methods: The knowledge portal was launched on February 1, 2022, and a Google Ad campaign was run between February 27, 2022, and March 26, 2022. Subsequently, a targeted social media campaign, an email campaign, and tailored research presentations were used to promote the website. Promotional activities ended on May 31, 2022. Website analytics were used to track a variety of actions including new users coming to the website, page views, and policy brief downloads. Statistical analysis was used to assess the efficacy of different approaches. Results: The campaign generated 2837 unique user visits to the knowledge portal and 4713 page views. In addition, the campaign generated 6.5 policy web page views/day and 0.7 policy brief downloads/day compared with 1.8 views/day and 0.5 downloads/day in the month following the campaign. The rate of policy brief page view conversions was significantly higher for Google Ads compared with other channels such as email (16.0 vs 5.4; P<.001) and tailored research presentations (16.0 vs 0.8; P<.001). The download conversion rate for Google Ads was significantly higher compared with social media (1.2 vs 0.1; P<.001) and knowledge brokering activities (1.2 vs 0.2; P<.001). By contrast, the download conversion rate for the email campaign was significantly higher than that for social media (1.0 vs 0.1; P<.001) and tailored research presentations (1.0 vs 0.2; P<.001). While Google Ads for this campaign cost an average of US \$2.09 per click, the cost per conversion was US \$11 per conversion to drive targeted policy web page views and US \$147 per conversion to drive policy brief downloads. While other approaches drove less traffic, those approaches were more targeted and cost-effective. Conclusions: Four tactics were tested to drive user engagement with policy briefs on the Project ASPEN knowledge portal. Google Ads was shown to be effective in driving a high volume of policy web page views but was ineffective in terms of relative costs. More targeted approaches such as email campaigns and tailored research presentations given to policy makers and advocates to promote the use of research evidence on the knowledge portal website are likely to be more effective when balancing goals and cost-effectiveness. ",
doi="10.2196/41997",
url="https://www.jmir.org/2023/1/e41997",
url="http://www.ncbi.nlm.nih.gov/pubmed/37379073"
}


@Article{info:doi/10.2196/45582,
author="Alexa, Maria Jennifer
and Richter, Matthias
and Bertsche, Thilo",
title="Enhancing Evidence-Based Pharmacy by Comparing the Quality of Web-Based Information Sources to the EVInews Database: Randomized Controlled Trial With German Community Pharmacists",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="21",
volume="25",
pages="e45582",
keywords="databases",
keywords="electronic information",
keywords="evidence-based pharmacy practice",
keywords="evidence-based pharmacy",
keywords="evidence-based practice",
keywords="external evidence",
keywords="health information quality",
keywords="information tools",
keywords="newsletter",
keywords="online survey",
keywords="pharmacist",
keywords="self-medication counseling",
keywords="self-medication",
keywords="utilization",
abstract="Background: Self-medication counseling in community pharmacies plays a crucial role in health care. Counseling advice should therefore be evidence-based. Web-based information and databases are commonly used as electronic information sources. EVInews is a self-medication--related information tool consisting of a database and monthly published newsletters for pharmacists. Little is known about the quality of pharmacists' electronic information sources for evidence-based self-medication counseling. Objective: Our aim was to investigate the quality of community pharmacists' web-based search results for self-medication--related content in comparison with the EVInews database, based on an adjusted quality score for pharmacists. Methods: After receiving ethics approval, we performed a quantitative web-based survey with a search task as a prospective randomized, controlled, and unblinded trial. For the search task, participants were instructed to search for evidence-based information to verify 6 health-related statements from 2 typical self-medication indications. Pharmacists across Germany were invited via email to participate. After providing written informed consent, they were automatically, randomly assigned to use either web-based information sources of their choice without the EVInews database (web group) or exclusively the EVInews database (EVInews group). The quality of the information sources that were used for the search task was then assessed by 2 evaluators using a quality score ranging from 100\% (180 points, all predefined criteria fulfilled) to 0\% (0 points, none of the predefined criteria fulfilled). In case of assessment discrepancies, an expert panel consisting of 4 pharmacists was consulted. Results: In total, 141 pharmacists were enrolled. In the Web group (n=71 pharmacists), the median quality score was 32.8\% (59.0 out of 180.0 points; IQR 23.0-80.5). In the EVInews group (n=70 pharmacists), the median quality score was significantly higher (85.3\%; 153.5 out of 180.0 points; P<.001) and the IQR was smaller (IQR 125.1-157.0). Fewer pharmacists completed the entire search task in the Web group (n=22) than in the EVInews group (n=46). The median time to complete the search task was not significantly different between the Web group (25.4 minutes) and the EVInews group (19.7 minutes; P=.12). The most frequently used web-based sources (74/254, 29.1\%) comprised tertiary literature. Conclusions: The median quality score of the web group was poor, and there was a significant difference in quality scores in favor of the EVInews group. Pharmacists' web-based and self-medication--related information sources often did not meet standard quality requirements and showed considerable variation in quality. Trial Registration: German Clinical Trials Register DRKS00026104; https://drks.de/search/en/trial/DRKS00026104 ",
doi="10.2196/45582",
url="https://www.jmir.org/2023/1/e45582",
url="http://www.ncbi.nlm.nih.gov/pubmed/37342085"
}


@Article{info:doi/10.2196/39484,
author="Lane, M. Jamil
and Habib, Daniel
and Curtis, Brenda",
title="Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data",
journal="J Med Internet Res",
year="2023",
month="Jun",
day="12",
volume="25",
pages="e39484",
keywords="Twitter",
keywords="public health interventions",
keywords="surveillance data",
keywords="health communication",
keywords="natural language processing",
abstract="Background: Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health--related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. Objective: The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. Methods: A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. Results: A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. Conclusions: Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions. ",
doi="10.2196/39484",
url="https://www.jmir.org/2023/1/e39484",
url="http://www.ncbi.nlm.nih.gov/pubmed/37307062"
}


@Article{info:doi/10.2196/43897,
author="Link, Elena
and Baumann, Eva",
title="A Comparison of Women's and Men's Web-Based Information-Seeking Behaviors About Gender-Related Health Information: Web-Based Survey Study of a Stratified German Sample",
journal="J Med Internet Res",
year="2023",
month="May",
day="17",
volume="25",
pages="e43897",
keywords="health information-seeking behavior",
keywords="HISB",
keywords="gender",
keywords="sex",
keywords="planned risk information seeking model",
keywords="subjective norms",
keywords="risk perceptions",
keywords="affective risk responses",
keywords="attitudes toward seeking",
keywords="perceived seeking control",
abstract="Background: Gender-sensitive approaches to health communication aim to integrate gender perspectives at all levels of communication, as an individual's biological sex and socially assigned gender identity have an impact on whether and how one acquires what type of health information. Due to the fast and low-cost opportunity to search for a wide range of information, the internet seems to be a particularly suitable place for gender-related health information about diseases of sex-specific organs and diseases where biological differences are associated with different health risks. Objective: This study aims to inform gender-related information provision and acquisition in 2 ways. The first objective was to provide a theory-driven analysis of web-based health information--seeking behavior (HISB) regarding gender-related issues. Therefore, the Planned Risk Information Seeking Model (PRISM), which is one of the most integrative models of HISB, was adapted and applied. Second, we asked for gender-specific motivational determinants of gender-related web-based HISB comparing the predictors in the groups of women and men. Methods: Data from a stratified web-based survey of the German population (N=3000) explained gender-related web-based HISB and influencing patterns comparing women and men. The applicability of PRISM to gender-related web-based HISB was tested using structural equation modeling and a multigroup comparison. Results: The results revealed PRISM as an effective framework for explaining gender-related web-based HISB. The model accounted for 28.8\% of the variance in gender-related web-based HISB. Gender-related subjective norms provided the most crucial explanatory power, followed by perceived seeking control. The multigroup comparison revealed differences in the model's explanatory power and the relevance of predictors of gender-related web-based HISB. The share of explained variances of web-based HISB is higher in men than in women. For men, norms were a more relevant promoting factor, whereas web-based HISB of women was more strongly associated with perceived seeking control. Conclusions: The results are crucial for gender-sensitive targeting strategies and suggest gender-related health information interventions that address gender-related subjective norms. Furthermore, programs (eg, web-based learning units) should be developed and offered to improve individuals' (perceived) abilities to perform web-based searches for health information, as those with higher control beliefs are more likely to access web-based information. ",
doi="10.2196/43897",
url="https://www.jmir.org/2023/1/e43897",
url="http://www.ncbi.nlm.nih.gov/pubmed/37195743"
}


@Article{info:doi/10.2196/44715,
author="Rochette, Annie
and Thomas, Aliki
and Salbach, M. Nancy
and Vachon, Brigitte
and Menon, Anita
and Poissant, Lise
and Boutin, Maurane
and Grad, Roland
and Pluye, Pierre",
title="Expected Health Benefits as the Ultimate Outcome of Information Available on Stroke Engine, a Knowledge Translation Stroke Rehabilitation Website: Web-Based Survey",
journal="JMIR Rehabil Assist Technol",
year="2023",
month="May",
day="8",
volume="10",
pages="e44715",
keywords="crowdsourcing",
keywords="health-related information",
keywords="internet",
keywords="knowledge translation",
keywords="rehabilitation",
keywords="stroke",
abstract="Background: Electronic knowledge resources are readily available and typically target different audiences, including health professionals and the public, that is, those with lived experience and their relatives. The knowledge-to-action framework, in combination with the information assessment method (IAM), considering both the value-of-information construct and the conceptual model of acquisition-cognition-application, can be used to support the evaluation process of such resources. As an example, Stroke Engine is an evidence-based knowledge translation resource in stroke rehabilitation (assessments and interventions) for health professionals and students as well as individuals who have sustained a stroke and their relatives. According to Google Analytics, the website is perused >10,000 times per week. Objective: With the overall aim to improve the content available on Stroke Engine, we documented Stroke Engine users' perceptions of situational relevance, cognitive impact, intention to use, and expected patient and health benefits regarding the information consulted. Methods: A web-based survey anchored in the IAM was made available via an invitation tab. The IAM is a validated questionnaire that is designed to assess the value of information. Sociodemographic characteristics were also collected, and a space for free-text comments was provided. Descriptive statistics were used, and thematic analysis was used for the free-text comments. Results: The sample consisted of 6634 respondents. Health professionals (3663/6634, 55.22\%) and students (2784/6634, 41.97\%) represented 97.18\% (6447/6634) of the total responses. The remaining 2.82\% (187/6634) of the responses were from individuals who had sustained a stroke (87/6634, 1.31\%) and their relatives (100/6634, 1.51\%). Regarding situational relevance, assessments (including selecting, obtaining, and interpreting results from a test) was the main topic searched by health professionals (1838/3364, 54.64\%) and students (1228/2437, 50.39\%), whereas general information on stroke rehabilitation was the top-ranked topic for nearly two-thirds of the individuals with stroke (45/76, 59\%) and their relatives (57/91, 63\%). Cognitive impact was characterized by learning something new. Intention to use was high (4572/6379, 71.67\%) among the respondents and varied in context (eg, refine a topic, research, class assignments, teaching, and education). Respondents commented on ways to improve content. Expected patient and health benefits such as improvement in health and well-being was the top-ranked category for all 4 subgroups, followed by the avoidance of unnecessary or inappropriate treatment for health professionals (183/623, 29.4\%) and a feeling of being reassured for individuals with stroke (26/75, 35\%) and their relatives (28/97, 29\%). Conclusions: Valuable feedback on Stroke Engine was obtained in terms of its accessibility, relevance for informational needs and retrieval, accuracy, and applicability; however, of utmost importance is the potential implementation of its evidence-based content in clinical practice and the perceived expected impact on patients, their relatives, and their health professionals. The feedback received allowed for corrections and the identification of key topics for further development. ",
doi="10.2196/44715",
url="https://rehab.jmir.org/2023/1/e44715",
url="http://www.ncbi.nlm.nih.gov/pubmed/37155228"
}


@Article{info:doi/10.2196/45281,
author="Simhadri, Suguna
and Yalamanchi, Sriha
and Stone, Sean
and Srinivasan, Mythily",
title="Perceptions on Oral Ulcers From Facebook Page Categories: Observational Study",
journal="JMIR Form Res",
year="2023",
month="May",
day="8",
volume="7",
pages="e45281",
keywords="oral ulcer",
keywords="internet",
keywords="Facebook",
keywords="information",
keywords="apthous stomatitis",
keywords="cold sore",
abstract="Background: Oral ulcers are a common condition affecting a considerable proportion of the population, and they are often associated with trauma and stress. They are very painful, and interfere with eating. As they are usually considered an annoyance, people may turn to social media for potential management options. Facebook is one of the most commonly accessed social media platforms and is the primary source of news information, including health information, for a significant percentage of American adults. Given the increasing importance of social media as a source of health information, potential remedies, and prevention strategies, it is essential to understand the type and quality of information available on Facebook regarding oral ulcers. Objective: The goal of our study was to evaluate information on recurrent oral ulcers that can be accessed via the most popular social media network---Facebook. Methods: We performed a keyword search of Facebook pages on 2 consecutive days in March 2022, using duplicate, newly created accounts, and then anonymized all posts. The collected pages were filtered, using predefined criteria to include only English-language pages wherein oral ulcer information was posted by the general public and to exclude pages created by professional dentists, associated professionals, organizations, and academic researchers. The selected pages were then screened for page origin and Facebook categories. Results: Our initial keyword search yielded 517 pages; interestingly however, only 112 (22\%) of pages had information relevant to oral ulcers, and 405 (78\%) had irrelevant information, with ulcers being mentioned in relation to other parts of the human body. Excluding professional pages and pages without relevant posts resulted in 30 pages, of which 9 (30\%) were categorized as ``health/beauty'' pages or as ``product/service'' pages, 3 (10\%) were categorized as ``medical \& health'' pages, and 5 (17\%) were categorized as ``community'' pages. Majority of the pages (22/30, 73\%) originated from 6 countries; most originated from the United States (7 pages), followed by India (6 pages). There was little information on oral ulcer prevention, long-term treatment, and complications. Conclusions: Facebook, in oral ulcer information dissemination, appears to be primarily used as an adjunct to business enterprises for marketing or for enhancing access to a product. Consequently, it was unsurprising that there was little information on oral ulcer prevention, long-term treatment, and complications. Although we made efforts to identify and select Facebook pages related to oral ulcers, we did not manually verify the authenticity or accuracy of the pages included in our analysis, potentially limiting the reliability of our findings or resulting in bias toward specific products or services. Although this work forms something of a pilot project, we plan to expand the project to encompass text mining for content analysis and include multiple social media platforms in the future. ",
doi="10.2196/45281",
url="https://formative.jmir.org/2023/1/e45281",
url="http://www.ncbi.nlm.nih.gov/pubmed/37155234"
}


@Article{info:doi/10.2196/43001,
author="Josey, Maria
and Gaid, Dina
and Bishop, D. Lisa
and Blackwood, Michael
and Najafizada, Maisam
and Donnan, R. Jennifer",
title="The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis",
journal="JMIR Infodemiology",
year="2023",
month="May",
day="2",
volume="3",
pages="e43001",
keywords="cannabis",
keywords="driving",
keywords="quality",
keywords="readability",
keywords="accuracy",
keywords="public education",
keywords="internet",
keywords="Google search",
keywords="analysis",
keywords="accessibility",
keywords="information",
keywords="evaluation",
keywords="tool",
keywords="data",
keywords="misinterpretation",
abstract="Background: The public perception of driving under the influence of cannabis (DUIC) is not consistent with current evidence. The internet is an influential source of information available for people to find information about cannabis. Objective: The purpose of this study was to assess the quality, readability, and accuracy of the information about DUIC found on the internet using the Google Canada search engine. Methods: A quantitative content analysis of the top Google search web pages was conducted to analyze the information available to the public about DUIC. Google searches were performed using keywords, and the first 20 pages were selected. Web pages or web-based resources were eligible if they had text on cannabis and driving in English. We assessed (1) the quality of information using the Quality Evaluation Scoring Tool (QUEST) and the presence of the Health on the Net (HON) code; (2) the readability of information using the Gunning Fox Index (GFI), Flesch Reading Ease Scale (FRES), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) scores; and (3) the accuracy of information pertaining to the effects of cannabis consumption, prevalence of DUIC, DUIC effects on driving ability, risk of collision, and detection by law enforcement using an adapted version of the 5Cs website evaluation tool. Results: A total of 82 web pages were included in the data analysis. The average QUEST score was 17.4 (SD 5.6) out of 28. The average readability scores were 9.7 (SD 2.3) for FKGL, 11.4 (SD 2.9) for GFI, 12.2 (SD 1.9) for SMOG index, and 49.9 (SD 12.3) for FRES. The readability scores demonstrated that 8 (9.8\%) to 16 (19.5\%) web pages were considered readable by the public. The accuracy results showed that of the web pages that presented information on each key topic, 96\% (22/23) of them were accurate about the effects of cannabis consumption; 97\% (30/31) were accurate about the prevalence of DUIC; 92\% (49/53) were accurate about the DUIC effects on driving ability; 80\% (41/51) were accurate about the risk of collision; and 71\% (35/49) were accurate about detection by law enforcement. Conclusions: Health organizations should consider health literacy of the public when creating content to help prevent misinterpretation and perpetuate prevailing misperceptions surrounding DUIC. Delivering high quality, readable, and accurate information in a way that is comprehensible to the public is needed to support informed decision-making. ",
doi="10.2196/43001",
url="https://infodemiology.jmir.org/2023/1/e43001"
}


@Article{info:doi/10.2196/44010,
author="Jiang, Zheng
and Yang, Xin
and Chen, Fei
and Liu, Jun",
title="Critical Analysis and Cross-Comparison Between English and Chinese Websites Providing Online Medical Information for Patients With Adenoid Hypertrophy: Cross-sectional Study",
journal="JMIR Form Res",
year="2023",
month="Apr",
day="24",
volume="7",
pages="e44010",
keywords="adenoid hypertrophy",
keywords="website quality",
keywords="critical analysis",
keywords="English and Chinese",
keywords="English",
keywords="Chinese",
keywords="patient",
keywords="internet",
keywords="online",
keywords="decisions",
keywords="medical issues",
keywords="airway obstruction",
keywords="airway",
keywords="accessibility",
keywords="quality",
abstract="Background: In the information era, patients can easily be misled by inaccurate internet content, thus making not well-informed decisions about medical issues. Adenoid hypertrophy, one of the most common causes of chronic upper airway obstruction in children and adolescents, may lead to serious complications, including sleep apnea and craniofacial change. There have been no critical studies about the quality of websites on adenoid hypertrophy, posing a challenge for users without a medical background to determine which website offers more reliable information. Moreover, the blockage of access to internet search tools such as Google, Yahoo, and others has created an isolated internet environment for the enormous user population in mainland China. Differences in internet legislation, the commercial environment, and culture are also likely to result in varied quality of online health information inside and outside mainland China. To date, no study has compared the quality difference between mainland Chinese and English websites. Objective: The aims of this study were to (1) analyze the quality of websites about adenoid hypertrophy accessible by patients, (2) investigate the quality differences between Chinese and English websites, (3) determine which type of website (eg, government-sponsored, health care provider) is more reliable in terms of medical information, and (4) determine whether the blockage of foreign websites is hindering users' accessibility to better-quality websites in mainland China. Methods: The first 100 websites (excluding advertisements) displayed on the top three search engines worldwide and in mainland China for the key search term ``enlarged adenoids'' were collected as the data source. The websites were evaluated based on accessibility, accountability, interactivity, structure, and content quality (accuracy, content coverage, and objectivity). Cohen $\kappa$ was calculated, and one-way ANOVA and the Kruskal-Wallis test were performed to compare the results between groups and subgroups. Results: The mean score for the content quality of English websites was significantly higher than that of Chinese websites (6.16 vs 4.94, P=.03 for Google, Bing, and Yahoo; 6.16 vs 4.16, P<.001 for Baidu, Sougou, and Bing China). Chinese users who are not influenced by the Internet Censorship System are more likely to access higher-quality online medical information (4.94 vs 4.16, P=.02). In within-group Student-Newman-Keuls q posthoc analysis, professional organization and government-sponsored websites were generally of better quality than other websites for both Chinese and English websites (P<.05). Conclusions: Generally, the English websites on adenoid hypertrophy are of better quality than Chinese websites; thus, Chinese users residing outside of the Chinese mainland are less influenced by inaccurate online medical information. ",
doi="10.2196/44010",
url="https://formative.jmir.org/2023/1/e44010",
url="http://www.ncbi.nlm.nih.gov/pubmed/37093621"
}


@Article{info:doi/10.2196/42297,
author="Cheng, Quan
and Lin, Yingru",
title="Multilevel Classification of Users' Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network",
journal="JMIR Form Res",
year="2023",
month="Apr",
day="20",
volume="7",
pages="e42297",
keywords="online medical health community",
keywords="multilevel classification",
keywords="graph convolutional network",
keywords="cardiovascular disease",
keywords="cardiovascular",
keywords="China",
keywords="online",
keywords="medical",
keywords="community",
keywords="behavior",
abstract="Background: Online medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users' questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users' information needs. Objective: Most online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users' needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users' needs in online medical and health communities so that users can perform more targeted information retrieval. Methods: Using the Chinese online medical and health community ``Qiuyi'' as an example, we crawled questions posted by users in the ``Cardiovascular Disease'' section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users' information needs label as the second-level label. Finally, by constructing a GCN model, users' questions were automatically classified, thus realizing the multilevel classification of users' needs. Results: Based on the empirical research of questions posted by users in the ``Cardiovascular Disease'' section of Qiuyi, the hierarchical classification of users' questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method na{\"i}ve Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users' needs, which in comparison with the multilevel classification model exhibited a great improvement. Conclusions: A multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users' information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications. ",
doi="10.2196/42297",
url="https://formative.jmir.org/2023/1/e42297",
url="http://www.ncbi.nlm.nih.gov/pubmed/37079346"
}


@Article{info:doi/10.2196/45268,
author="Costello, Jeremy
and Kaur, Manpreet
and Reformat, Z. Marek
and Bolduc, V. Francois",
title="Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study",
journal="J Med Internet Res",
year="2023",
month="Apr",
day="17",
volume="25",
pages="e45268",
keywords="knowledge graph",
keywords="natural language processing",
keywords="neurodevelopmental disorders",
keywords="autism spectrum disorder",
keywords="intellectual disability",
keywords="attention deficit hyperactivity disorder",
keywords="named entity recognition",
keywords="topic modeling",
keywords="aggregation operator",
abstract="Background: Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families. NDDs affect up to 18\% of the population and have major social and economic impacts. The current limitations in communicating information for individuals with NDDs include the absence of shared terminology and the lack of efficient labeling processes for web resources. Because of these limitations, health professionals, support groups, and families are unable to share, combine, and access resources. Objective: We aimed to develop a natural language--based pipeline to label resources by leveraging standard and free-text vocabularies obtained through text analysis, and then represent those resources as a weighted knowledge graph. Methods: Using a combination of experts and service/organization databases, we created a data set of web resources for NDDs. Text from these websites was scraped and collected into a corpus of textual data on NDDs. This corpus was used to construct a knowledge graph suitable for use by both experts and nonexperts. Named entity recognition, topic modeling, document classification, and location detection were used to extract knowledge from the corpus. Results: We developed a resource annotation pipeline using diverse natural language processing algorithms to annotate web resources and stored them in a structured knowledge graph. The graph contained 78,181 annotations obtained from the combination of standard terminologies and a free-text vocabulary obtained using topic modeling. An application of the constructed knowledge graph is a resource search interface using the ordered weighted averaging operator to rank resources based on a user query. Conclusions: We developed an automated labeling pipeline for web resources on NDDs. This work showcases how artificial intelligence--based methods, such as natural language processing and knowledge graphs for information representation, can enhance knowledge extraction and mobilization, and could be used in other fields of medicine. ",
doi="10.2196/45268",
url="https://www.jmir.org/2023/1/e45268",
url="http://www.ncbi.nlm.nih.gov/pubmed/37067865"
}


@Article{info:doi/10.2196/39891,
author="Van Heel, Lou Kasi
and Nelson, Anna
and Handysides, Daniel
and Shah, Huma",
title="The Factors Associated With Confidence in Using the Internet to Access Health Information: Cross-sectional Data Analysis",
journal="JMIR Form Res",
year="2023",
month="Apr",
day="11",
volume="7",
pages="e39891",
keywords="confidence",
keywords="health information access",
keywords="health information seeking",
keywords="health information sources",
keywords="internet",
keywords="health information",
abstract="Background: Confidence in health information access is a measure of the perceived ability to obtain health information. One's beliefs or perceived ability to access health information is particularly important in understanding trends in health care access. Previous literature has found that access to health information is lowest among society's most vulnerable population groups. These groups include older, less educated, and low-income populations. While health confidence has previously been used as a scale to measure health outcomes, additional research is needed describing the demographic factors associated with users' confidence in health information access. This may be a key component of health information seeking that affects beneficial health outcomes such as prevention and treatment. Objective: This study examines the demographic factors associated with the levels of confidence in using the internet to access health information for adults 18 years and older in the United States. Methods: Using a cross-sectional design, secondary data from the Health Information National Trends Survey (HINTS) 5, Cycle 3 (2019) were analyzed (N=5374). An ordinal regression stratified by internet use was used to determine the association between demographic characteristics and level of confidence in health information access. Results: When the internet is the primary source for health information, high school graduates (adjusted odds ratio [AOR] 0.58, 95\% CI 0.37-0.89) compared to those with a college degree or more had significantly lower odds of being confident in obtaining health information. In addition, non-Hispanic Asian participants (AOR 0.44, 95\% CI 0.24-0.82) compared to non-Hispanic White participants, male participants (AOR 0.72, 95\% CI 0.54-0.97) compared to female participants, and those who made between US \$20,000-\$35,000 annually (AOR 0.55, 95\% CI 0.31-0.98) compared to those who made US \$75,000 or more annually had significantly lower odds of being confident in obtaining health information via the internet. Moreover, when the internet is the primary source for health information, those with health insurance had significantly higher odds of being confident in obtaining health information (AOR 2.91, 95\% CI 1.58-5.34) compared to those who do not have health insurance. Lastly, a significant association was observed between confidence in health information access and primary health information source and frequency of visiting a health care provider. Conclusions: Confidence in accessing health information can differ by individual demographics. Accessing health-related information from the internet has become increasingly more common and can provide insight into health information-seeking behaviors. Further exploration of these factors can inform the science of health education by providing deeper insight into improving access to health information for vulnerable populations. ",
doi="10.2196/39891",
url="https://formative.jmir.org/2023/1/e39891",
url="http://www.ncbi.nlm.nih.gov/pubmed/37040161"
}


@Article{info:doi/10.2196/39054,
author="Shah, B. Ami
and Oyegun, Eghosa
and Hampton, Brett William
and Neri, Antonio
and Maddox, Nicole
and Raso, Danielle
and Sandhu, Paramjit
and Patel, Anita
and Koonin, M. Lisa
and Lee, Leslie
and Roper, Lauren
and Whitfield, Geoffrey
and Siegel, A. David
and Koumans, H. Emily",
title="Engagement With the Centers for Disease Control and Prevention Coronavirus Self-Checker and Guidance Provided to Users in the United States From March 23, 2020, to April 19, 2021: Thematic and Trend Analysis",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="10",
volume="25",
pages="e39054",
keywords="COVID-19",
keywords="automated symptom checker",
keywords="Self-Checker",
keywords="triage",
keywords="medical care",
keywords="online information seeking",
keywords="clinical assessment tool",
abstract="Background: In 2020, at the onset of the COVID-19 pandemic, the United States experienced surges in healthcare needs, which challenged capacity throughout the healthcare system. Stay-at-home orders in many jurisdictions, cancellation of elective procedures, and closures of outpatient medical offices disrupted patient access to care. To inform symptomatic persons about when to seek care and potentially help alleviate the burden on the healthcare system, Centers for Disease Control and Prevention (CDC) and partners developed the CDC Coronavirus Self-Checker (``Self-Checker''). This interactive tool assists individuals seeking information about COVID-19 to determine the appropriate level of care by asking demographic, clinical, and nonclinical questions during an online ``conversation.'' Objective: This paper describes user characteristics, trends in use, and recommendations delivered by the Self-Checker between March 23, 2020, and April 19, 2021, for pursuing appropriate levels of medical care depending on the severity of user symptoms. Methods: User characteristics and trends in completed conversations that resulted in a care message were analyzed. Care messages delivered by the Self-Checker were manually classified into three overarching conversation themes: (1) seek care immediately; (2) take no action, or stay home and self-monitor; and (3) conversation redirected. Trends in 7-day averages of conversations and COVID-19 cases were examined with development and marketing milestones that potentially impacted Self-Checker user engagement. Results: Among 16,718,667 completed conversations, the Self-Checker delivered recommendations for 69.27\% (n=11,580,738) of all conversations to ``take no action, or stay home and self-monitor''; 28.8\% (n=4,822,138) of conversations to ``seek care immediately''; and 1.89\% (n=315,791) of conversations were redirected to other resources without providing any care advice. Among 6.8 million conversations initiated for self-reported sick individuals without life-threatening symptoms, 59.21\% resulted in a recommendation to ``take no action, or stay home and self-monitor.'' Nearly all individuals (99.8\%) who were not sick were also advised to ``take no action, or stay home and self-monitor.'' Conclusions: The majority of Self-Checker conversations resulted in advice to take no action, or stay home and self-monitor. This guidance may have reduced patient volume on the medical system; however, future studies evaluating patients' satisfaction, intention to follow the care advice received, course of action, and care modality pursued could clarify the impact of the Self-Checker and similar tools during future public health emergencies. ",
doi="10.2196/39054",
url="https://www.jmir.org/2023/1/e39054",
url="http://www.ncbi.nlm.nih.gov/pubmed/36745776"
}


@Article{info:doi/10.2196/36765,
author="Mallakin, Maryam
and Dery, Christina
and Vaillancourt, Samuel
and Gupta, Sahil
and Sellen, Katherine",
title="Web-Based Co-design in Health Care: Considerations for Renewed Participation",
journal="Interact J Med Res",
year="2023",
month="Mar",
day="3",
volume="12",
pages="e36765",
keywords="web-based design research",
keywords="co-design",
keywords="web-based co-design",
keywords="virtual platform",
keywords="virtual learning platforms",
keywords="internet research ethics",
keywords="collaboration",
keywords="health communication",
keywords="sensemaking",
keywords="health design",
keywords="tangible tools and games",
doi="10.2196/36765",
url="https://www.i-jmr.org/2023/1/e36765",
url="http://www.ncbi.nlm.nih.gov/pubmed/36595738"
}


@Article{info:doi/10.2196/40518,
author="Robinson, Eric
and Jones, Andrew",
title="Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study",
journal="JMIR Form Res",
year="2023",
month="Mar",
day="3",
volume="7",
pages="e40518",
keywords="alcohol",
keywords="COVID-19",
keywords="hangover",
keywords="Google Trends",
keywords="social media",
keywords="public health",
keywords="online information",
keywords="alcohol use",
keywords="internet search",
abstract="Background: It is unclear whether heavy alcohol use and associated hangover symptoms changed as a result of the COVID-19 pandemic. Due to a lack of available accurate and nonretrospective self-reported data, it is difficult to directly assess hangover symptoms during the COVID-19 pandemic. Objective: This study aimed to examine whether alcohol-induced hangover-related internet searches (eg, ``how to cure a hangover?'') increased, decreased, or remained the same in England before versus during the COVID-19 pandemic (2020-2021) and during periods of national lockdown. Secondary aims were to examine if hangover-related internet searches in England differed compared to a country that did not impose similar COVID-19 lockdown restrictions. Methods: Using historical data from Google Trends for England, we compared the relative search volume (RSV) of hangover-related searches in the years before (2016-2019) versus during the COVID-19 pandemic (2020-2021), as well as in periods of national lockdown versus the same periods in 2016-2019. We also compared the RSV of hangover-related searches during the same time frames in a European country that did not introduce national COVID-19 lockdowns at the beginning of the pandemic (Sweden). Hangover-related search terms were identified through consultation with a panel of alcohol researchers and a sample from the general public. Statistical analyses were preregistered prior to data collection. Results: There was no overall significant difference in the RSV of hangover-related terms in England during 2016-2019 versus 2020-2021 (P=.10; robust d=0.02, 95\% CI 0.00-0.03). However, during national lockdowns, searches for hangover-related terms were lower, particularly during the first national lockdown in England (P<.001; d=.19, 95\% CI 0.16-0.24; a 44\% relative decrease). In a comparison country that did not introduce a national lockdown in the early stages of the pandemic (Sweden), there was no significant decrease in hangover-related searches during the same time period (P=.06). However, across both England and Sweden, during later periods of COVID-19 restrictions in 2020 and 2021, the RSV of hangover-related terms was lower than that in the same periods during 2016-2019. Exploratory analyses revealed that national monthly variation in alcohol sales both before and during the COVID-19 pandemic were positively correlated with the frequency of hangover-related searches, suggesting that changes in hangover-related searches may act as a proxy for changes in alcohol consumption. Conclusions: Hangover-related internet searches did not differ before versus during the COVID-19 pandemic in England but did reduce during periods of national lockdown. Further research is required to confirm how changes in hangover-related search volume relate to heavy episodic alcohol use. Trial Registration: Open Science Framework 2Y86E; https://osf.io/2Y86E ",
doi="10.2196/40518",
url="https://formative.jmir.org/2023/1/e40518",
url="http://www.ncbi.nlm.nih.gov/pubmed/36827489"
}


@Article{info:doi/10.2196/44741,
author="Mitsutake, Seigo
and Takahashi, Yoshimitsu
and Otsuki, Aki
and Umezawa, Jun
and Yaguchi-Saito, Akiko
and Saito, Junko
and Fujimori, Maiko
and Shimazu, Taichi
and ",
title="Chronic Diseases and Sociodemographic Characteristics Associated With Online Health Information Seeking and Using Social Networking Sites: Nationally Representative Cross-sectional Survey in Japan",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="2",
volume="25",
pages="e44741",
keywords="chronic diseases",
keywords="cross-sectional study",
keywords="eHealth literacy, health communication",
keywords="internet, social networking",
abstract="Background: In an aging society, worsening chronic diseases increase the burden on patients and the health care system. Using online health information including health information via social networking sites (SNSs), such as Facebook and YouTube, may play an important role in the self-management of chronic diseases and health promotion for internet users. Objective: This study aims to improve strategies for promoting access to reliable information for the self-management of chronic diseases via the internet, and to identify populations facing barriers to using the internet for health, we examined chronic diseases and characteristics associated with online health information seeking and the use of SNSs. Methods: This study used data from the INFORM Study 2020, which was a nationally representative cross-sectional postal mail survey conducted using a self-administered questionnaire in 2020. The dependent variables were online health information seeking and SNS use. Online health information seeking was assessed using 1 question about whether respondents used the internet to find health or medical information. SNS use was assessed by inquiring about the following 4 aspects: visiting SNSs, sharing health information on SNSs, writing in an online diary or blog, and watching a health-related video on YouTube. The independent variables were 8 chronic diseases. Other independent variables were sex, age, education status, work, marital status, household income, health literacy, and self-reported health status. We conducted a multivariable logistic regression model adjusted for all independent variables to examine the associations of chronic diseases and other variables with online health information seeking and SNS use. Results: The final sample for analysis comprised 2481 internet users. Hypertension or high blood pressure, chronic lung diseases, depression or anxiety disorder, and cancer were reported by 24.5\%, 10.1\%, 7.7\%, and 7.2\% of respondents, respectively. The odds ratio of online health information seeking among respondents with cancer was 2.19 (95\% CI 1.47-3.27) compared with that among those without cancer, and the odds ratio among those with depression or anxiety disorder was 2.27 (95\% CI 1.46-3.53) compared with that among those without. Further, the odds ratio for watching a health-related YouTube video among those with chronic lung diseases was 1.42 (95\% CI 1.05-1.93) compared with that among those without these diseases. Women, younger age, higher level of education, and high health literacy were positively associated with online health information seeking and SNS use. Conclusions: For patients with cancer, strategies for promoting access to websites with reliable cancer-related information as well as access among patients with chronic lung diseases to YouTube videos providing reliable information may be beneficial for the management of these diseases. Moreover, it is important to improve the online environment to encourage men, older adults, internet users with lower education levels, and those with low health literacy to access online health information. ",
doi="10.2196/44741",
url="https://www.jmir.org/2023/1/e44741",
url="http://www.ncbi.nlm.nih.gov/pubmed/36862482"
}


@Article{info:doi/10.2196/43758,
author="Gottumukkala, Madhu",
title="Design, Development, and Evaluation of an Automated Solution for Electronic Information Exchange Between Acute and Long-term Postacute Care Facilities: Design Science Research",
journal="JMIR Form Res",
year="2023",
month="Feb",
day="17",
volume="7",
pages="e43758",
keywords="information exchange",
keywords="interoperability",
keywords="care transition",
keywords="health information technology",
keywords="health information exchange",
keywords="open standards",
keywords="long-term and postacute care",
keywords="LTPAC",
keywords="design science research",
abstract="Background: Information exchange is essential for transitioning high-quality care between care settings. Inadequate or delayed information exchange can result in medication errors, missed test results, considerable delays in care, and even readmissions. Unfortunately, long-term and postacute care facilities often lag behind other health care facilities in adopting health information technologies, increasing difficulty in facilitating care transitions through electronic information exchange. The research gap is most evident when considering the implications of the inability to electronically transfer patients' health records between these facilities. Objective: This study aimed to design and evaluate an open standards--based interoperability solution that facilitates seamless bidirectional information exchange between acute care and long-term and postacute care facilities using 2 vendor electronic health record (EHR) systems. Methods: Using the design science research methodology, we designed an interoperability solution that improves the bidirectional information exchange between acute care and long-term care (LTC) facilities using different EHR systems. Different approaches were applied in the study with a focus on the relevance cycle, including eliciting detailed requirements from stakeholders in the health system who understand the complex data formats, constraints, and workflows associated with transferring patient records between 2 different EHR systems. We performed literature reviews and sought experts in the health care industry from different organizations with a focus on the rigor cycle to identify the components relevant to the interoperability solution. The design cycle focused on iterating between the core activities of implementing and evaluating the proposed artifact. The artifact was evaluated at a health care organization with a combined footprint of acute and postacute care operations using 2 different EHR systems. Results: The resulting interoperability solution offered integrations with source systems and was proven to facilitate bidirectional information exchange for patients transferring between an acute care facility using an Epic EHR system and an LTC facility using a PointClickCare EHR system. This solution serves as a proof of concept for bidirectional data exchange between Epic and PointClickCare for medications, yet the solution is designed to expand to additional data elements such as allergies, problem lists, and diagnoses. Conclusions: Historically, the interoperability topic has centered on hospital-to-hospital data exchange, making it more challenging to evaluate the efficacy of data exchange between other care settings. In acute and LTC settings, there are differences in patients' needs and delivery of care workflows that are distinctly unique. In addition, the health care system's components that offer long-term and acute care in the United States have evolved independently and separately. This study demonstrates that the interoperability solution improves the information exchange between acute and LTC facilities by simplifying data transfer, eliminating manual processes, and reducing data discrepancies using a design science research methodology. ",
doi="10.2196/43758",
url="https://formative.jmir.org/2023/1/e43758",
url="http://www.ncbi.nlm.nih.gov/pubmed/36800213"
}


@Article{info:doi/10.2196/40466,
author="Bergmo, Strand Trine
and Sandsdalen, Vilde
and Manskow, Sollid Unn
and Sm{\aa}brekke, Lars
and Waaseth, Marit",
title="Internet Use for Obtaining Medicine Information: Cross-sectional Survey",
journal="JMIR Form Res",
year="2023",
month="Feb",
day="2",
volume="7",
pages="e40466",
keywords="credibility",
keywords="credible",
keywords="cross-sectional",
keywords="eHealth",
keywords="health information",
keywords="information behavior",
keywords="information retrieval",
keywords="information science",
keywords="information seeking",
keywords="internet",
keywords="medication",
keywords="medicine information",
keywords="misinformation",
keywords="patient education",
keywords="pharmaceutical",
keywords="pharmacist",
keywords="pharmacy",
keywords="survey",
keywords="trust",
keywords="web-based information",
abstract="Background: The internet is increasingly being used as a source of medicine-related information. People want information to facilitate decision-making and self-management, and they tend to prefer the internet for ease of access. However, it is widely acknowledged that the quality of web-based information varies. Poor interpretation of medicine information can lead to anxiety and poor adherence to drug therapy. It is therefore important to understand how people search, select, and trust medicine information. Objective: The objectives of this study were to establish the extent of internet use for seeking medicine information among Norwegian pharmacy customers, analyze factors associated with internet use, and investigate the level of trust in different sources and websites. Methods: This is a cross-sectional study with a convenience sample of pharmacy customers recruited from all but one community pharmacy in Troms{\o}, a medium size municipality in Norway (77,000 inhabitants). Persons (aged ?16 years) able to complete a questionnaire in Norwegian were asked to participate in the study. The recruitment took place in September and October 2020. Due to COVID-19 restrictions, social media was also used to recruit medicine users. Results: A total of 303 respondents reported which sources they used to obtain information about their medicines (both prescription and over the counter) and to what extent they trusted these sources. A total of 125 (41.3\%) respondents used the internet for medicine information, and the only factor associated with internet use was age. The odds of using the internet declined by 5\% per year of age (odds ratio 0.95, 95\% CI 0.94-0.97; P=.048). We found no association between internet use and gender, level of education, or regular medicine use. The main purpose reported for using the internet was to obtain information about side effects. Other main sources of medicine information were physicians (n=191, 63\%), pharmacy personnel (n=142, 47\%), and medication package leaflets (n=124, 42\%), while 36 (12\%) respondents did not obtain medicine information from any sources. Note that 272 (91\%) respondents trusted health professionals as a source of medicine information, whereas 58 (46\%) respondents who used the internet trusted the information they found on the internet. The most reliable websites were the national health portals and other official health information sites. Conclusions: Norwegian pharmacy customers use the internet as a source of medicine information, but most still obtain medicine information from health professionals and packet leaflets. People are aware of the potential for misinformation on websites, and they mainly trust high-quality sites run by health authorities. ",
doi="10.2196/40466",
url="https://formative.jmir.org/2023/1/e40466",
url="http://www.ncbi.nlm.nih.gov/pubmed/36729577"
}


@Article{info:doi/10.2196/32161,
author="B{\'e}rub{\'e}, Caterina
and Kovacs, Ferenc Zsolt
and Fleisch, Elgar
and Kowatsch, Tobias",
title="Reliability of Commercial Voice Assistants' Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information",
journal="J Med Internet Res",
year="2021",
month="Dec",
day="20",
volume="23",
number="12",
pages="e32161",
keywords="voice assistants",
keywords="conversational agents",
keywords="health literacy",
keywords="noncommunicable diseases",
keywords="mobile phone",
keywords="smart speaker",
keywords="smart display",
keywords="evaluation",
keywords="protocol",
keywords="assistant",
keywords="agent",
keywords="literacy",
keywords="audio",
keywords="health information",
keywords="management",
keywords="factorial",
keywords="information source",
abstract="Background: Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients' access to health-related information through efficient and accessible channels, such as commercial voice assistants (VAs), may support the patients' ability to make health-related decisions and manage their chronic conditions. Objective: This study aims to evaluate the reliability of the most common VAs (ie, Amazon Alexa, Apple Siri, and Google Assistant) in responding to questions about management of the main NCD. Methods: We generated health-related questions based on frequently asked questions from health organization, government, medical nonprofit, and other recognized health-related websites about conditions associated with Alzheimer's disease (AD), lung cancer (LCA), chronic obstructive pulmonary disease, diabetes mellitus (DM), cardiovascular disease, chronic kidney disease (CKD), and cerebrovascular accident (CVA). We then validated them with practicing medical specialists, selecting the 10 most frequent ones. Given the low average frequency of the AD-related questions, we excluded such questions. This resulted in a pool of 60 questions. We submitted the selected questions to VAs in a 3{\texttimes}3{\texttimes}6 fractional factorial design experiment with 3 developers (ie, Amazon, Apple, and Google), 3 modalities (ie, voice only, voice and display, display only), and 6 diseases. We assessed the rate of error-free voice responses and classified the web sources based on previous research (ie, expert, commercial, crowdsourced, or not stated). Results: Google showed the highest total response rate, followed by Amazon and Apple. Moreover, although Amazon and Apple showed a comparable response rate in both voice-and-display and voice-only modalities, Google showed a slightly higher response rate in voice only. The same pattern was observed for the rate of expert sources. When considering the response and expert source rate across diseases, we observed that although Google remained comparable, with a slight advantage for LCA and CKD, both Amazon and Apple showed the highest response rate for LCA. However, both Google and Apple showed most often expert sources for CVA, while Amazon did so for DM. Conclusions: Google showed the highest response rate and the highest rate of expert sources, leading to the conclusion that Google Assistant would be the most reliable tool in responding to questions about NCD management. However, the rate of expert sources differed across diseases. We urge health organizations to collaborate with Google, Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions on NCD management across the different diseases. ",
doi="10.2196/32161",
url="https://www.jmir.org/2021/12/e32161",
url="http://www.ncbi.nlm.nih.gov/pubmed/34932003"
}


@Article{info:doi/10.2196/34051,
author="Kudlow, Paul
and Brown, Tashauna
and Eysenbach, Gunther",
title="Citation Advantage of Promoted Articles in a Cross-Publisher Distribution Platform: 36-Month Follow-up to a Randomized Controlled Trial",
journal="J Med Internet Res",
year="2021",
month="Dec",
day="10",
volume="23",
number="12",
pages="e34051",
keywords="knowledge translation",
keywords="knowledge",
keywords="dissemination",
keywords="digital knowledge translation",
keywords="digital publishing",
keywords="e-publishing",
keywords="open access",
keywords="scientometrics",
keywords="infometrics",
abstract="Background: There are limited evidence-based strategies that have been shown to increase the rate at which peer-reviewed articles are cited. In a previously reported randomized controlled trial, we demonstrated that promotion of article links in an online cross-publisher distribution platform (TrendMD) persistently augments citation rates after 12 months, leading to a statistically significant 50\% increase in citations relative to the control. Objective: This study aims to investigate if the citation advantage of promoted articles upholds after 36 months. Methods: A total of 3200 published articles in 64 peer-reviewed journals across 8 subject areas were block randomized at the subject level to either the TrendMD group (n=1600) or the control group (n=1600) of the study. Articles were promoted in the TrendMD Network for 6 months. We compared the citation rates in both groups after 36 months. Results: At 36 months, we found the citation advantage endured; articles randomized to TrendMD showed a 28\% increase in mean citations relative to the control. The difference in mean citations at 36 months for articles randomized to TrendMD versus the control was 10.52 (95\% CI 3.79-17.25) and was statistically significant (P=.001). Conclusions: To our knowledge, this is the first randomized controlled trial to demonstrate how a postpublication article promotion intervention can be used to persistently augment citations of peer-reviewed articles. TrendMD is an efficient digital tool for knowledge translation and dissemination to targeted audiences to facilitate the uptake of research. ",
doi="10.2196/34051",
url="https://www.jmir.org/2021/12/e34051",
url="http://www.ncbi.nlm.nih.gov/pubmed/34890350"
}


@Article{info:doi/10.2196/31358,
author="Koren, Ainat
and Alam, Ul Mohammad Arif
and Koneru, Sravani
and DeVito, Alexa
and Abdallah, Lisa
and Liu, Benyuan",
title="Nursing Perspectives on the Impacts of COVID-19: Social Media Content Analysis",
journal="JMIR Form Res",
year="2021",
month="Dec",
day="10",
volume="5",
number="12",
pages="e31358",
keywords="mental health",
keywords="information retrieval",
keywords="coronavirus",
keywords="COVID-19",
keywords="nursing",
keywords="nurses",
keywords="health care workers",
keywords="pandemic",
keywords="impact",
keywords="social media analytics",
abstract="Background: Nurses are at the forefront of the COVID-19 pandemic. During the pandemic, nurses have faced an elevated risk of exposure and have experienced the hazards related to a novel virus. While being heralded as lifesaving heroes on the front lines of the pandemic, nurses have experienced more physical, mental, and psychosocial problems as a consequence of the COVID-19 outbreak. Social media discussions by nursing professionals participating in publicly formed Facebook groups constitute a valuable resource that offers longitudinal insights. Objective: This study aimed to explore how COVID-19 impacted nurses through capturing public sentiments expressed by nurses on a social media discussion platform and how these sentiments changed over time. Methods: We collected over 110,993 Facebook discussion posts and comments in an open COVID-19 group for nurses from March 2020 until the end of November 2020. Scraping of deidentified offline HTML tags on social media posts and comments was performed. Using subject-matter expert opinions and social media analytics (ie, topic modeling, information retrieval, and sentiment analysis), we performed a human-in-a-loop analysis of nursing professionals' key perspectives to identify trends of the COVID-19 impact among at-risk nursing communities. We further investigated the key insights of the trends of the nursing professionals' perspectives by detecting temporal changes of comments related to emotional effects, feelings of frustration, impacts of isolation, shortage of safety equipment, and frequency of safety equipment uses. Anonymous quotes were highlighted to add context to the data. Results: We determined that COVID-19 impacted nurses' physical, mental, and psychosocial health as expressed in the form of emotional distress, anger, anxiety, frustration, loneliness, and isolation. Major topics discussed by nurses were related to work during a pandemic, misinformation spread by the media, improper personal protective equipment (PPE), PPE side effects, the effects of testing positive for COVID-19, and lost days of work related to illness. Conclusions: Public Facebook nursing groups are venues for nurses to express their experiences, opinions, and concerns and can offer researchers an important insight into understanding the COVID-19 impact on health care workers. ",
doi="10.2196/31358",
url="https://formative.jmir.org/2021/12/e31358",
url="http://www.ncbi.nlm.nih.gov/pubmed/34623957"
}


@Article{info:doi/10.2196/31791,
author="Abdel-Razig, Sawsan
and Anglade, Pascale
and Ibrahim, Halah",
title="Impact of the COVID-19 Pandemic on a Physician Group's WhatsApp Chat: Qualitative Content Analysis",
journal="JMIR Form Res",
year="2021",
month="Dec",
day="7",
volume="5",
number="12",
pages="e31791",
keywords="WhatsApp",
keywords="social media",
keywords="physician",
keywords="pandemic",
keywords="COVID-19",
keywords="qualitative",
keywords="communication",
keywords="misinformation",
keywords="information-seeking behavior",
keywords="information seeking",
keywords="information sharing",
keywords="content analysis",
keywords="community",
abstract="Background: Social media has emerged as an effective means of information sharing and community building among health professionals. The utility of these platforms is likely heightened during times of health system crises and global uncertainty. Studies have demonstrated that physicians' social media platforms serve to bridge the gap of information between on-the-ground experiences of health care workers and emerging knowledge. Objective: The primary aim of this study was to characterize the use of a physician WhatsApp (WhatsApp LLC) group chat during the early months of the COVID-19 pandemic. Methods: Through the lens of the social network theory, we performed a qualitative content analysis of the posts of a women physician WhatsApp group located in the United Arab Emirates between February 1, 2020, and May 31, 2020, that is, during the initial surge of COVID-19 cases. Results: There were 6101 posts during the study period, which reflected a 2.6-fold increase in platform use when compared with platform use in the year prior. A total of 8 themes and 9 subthemes were described. The top 3 uses of the platform were requests for information (posts: 2818/6101, 46.2\%), member support and promotion (posts: 988/6101, 16.2\%), and information sharing (posts: 896/6101, 14.7\%). A substantial proportion of posts were related to COVID-19 (2653/6101, 43.5\%), with the most popular theme being requests for logistical (nonmedical) information. Among posts containing COVID-19--related medical information, it was notable that two-thirds (571/868, 65.8\%) of these posts were from public mass media or unverified sources. Conclusions: Health crises can potentiate the use of social media platforms among physicians. This reflects physicians' tendency to turn to these platforms for information sharing and community building purposes. However, important questions remain regarding the accuracy and credibility of the information shared. Our findings suggest that the training of physicians in social media practices and information dissemination may be needed. ",
doi="10.2196/31791",
url="https://formative.jmir.org/2021/12/e31791",
url="http://www.ncbi.nlm.nih.gov/pubmed/34784291"
}


@Article{info:doi/10.2196/26065,
author="Nabo?ny, Aleksandra
and Balcerzak, Bart?omiej
and Wierzbicki, Adam
and Morzy, Miko?aj
and Chlabicz, Ma?gorzata",
title="Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning",
journal="JMIR Med Inform",
year="2021",
month="Nov",
day="26",
volume="9",
number="11",
pages="e26065",
keywords="active annotation",
keywords="credibility",
keywords="web-based medical information",
keywords="fake news",
abstract="Background: The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web. Objective: The aim of this study is to present a comprehensive framework for designing and curating machine learning training data sets for web-based medical information credibility assessment. We show how to construct the annotation process. Our main objective is to support researchers from the medical and computer science communities. We offer guidelines on the preparation of data sets for machine learning models that can fight medical misinformation. Methods: We begin by providing the annotation protocol for medical experts involved in medical sentence credibility evaluation. The protocol is based on a qualitative study of our experimental data. To address the problem of insufficient initial labels, we propose a preprocessing pipeline for the batch of sentences to be assessed. It consists of representation learning, clustering, and reranking. We call this process active annotation. Results: We collected more than 10,000 annotations of statements related to selected medical subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, and food allergy testing) for less than US \$7000 by employing 9 highly qualified annotators (certified medical professionals), and we release this data set to the general public. We developed an active annotation framework for more efficient annotation of noncredible medical statements. The application of qualitative analysis resulted in a better annotation protocol for our future efforts in data set creation. Conclusions: The results of the qualitative analysis support our claims of the efficacy of the presented method. ",
doi="10.2196/26065",
url="https://medinform.jmir.org/2021/11/e26065",
url="http://www.ncbi.nlm.nih.gov/pubmed/34842547"
}


@Article{info:doi/10.2196/25394,
author="Santisteban-Espejo, Antonio
and Martin-Piedra, Angel Miguel
and Campos, Antonio
and Moran-Sanchez, Julia
and Cobo, J. Manuel
and Pacheco-Serrano, I. Ana
and Moral-Munoz, A. Jose",
title="Information and Scientific Impact of Advanced Therapies in the Age of Mass Media: Altmetrics-Based Analysis of Tissue Engineering",
journal="J Med Internet Res",
year="2021",
month="Nov",
day="26",
volume="23",
number="11",
pages="e25394",
keywords="advanced therapies",
keywords="tissue engineering",
keywords="scientometrics",
keywords="altmetrics",
keywords="online",
keywords="web",
keywords="communication of science",
abstract="Background: Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-stage organs. Its development has taken place since the last decade of the 20th century, entailing a clinical revolution. TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact. Objective: The objective of this study was to evaluate the online dimension of TE documents from 2012 to 2018 using metadata obtained from the Web of Science (WoS) and Altmetric and to develop a prediction equation for the impact of TE documents from altmetric scores. Methods: We analyzed 10,112 TE documents through descriptive and statistical methods. First, the TE temporal evolution was exposed for WoS and 15 online platforms (news, blogs, policy, Twitter, patents, peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q\&A, video, and Mendeley Readers). The 10 most cited TE original articles were ranked according to the normalized WoS citations and the normalized Altmetric Attention Score. Second, to better comprehend the TE online framework, correlation and factor analyses were performed based on the suitable results previously obtained for the Bartlett sphericity and Kaiser--Meyer--Olkin tests. Finally, the linear regression model was applied to elucidate the relation between academics and online media and to construct a prediction equation for TE from altmetrics data. Results: TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers, and Altmetric Scores. However, WoS and Altmetric rankings for the most cited documents clearly differ. When compared, the best correlation results were obtained for Mendeley Readers and WoS ($\rho$=0.71). In addition, the factor analysis identified 6 factors that could explain the previously observed differences between academic institutions and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than 40\% of TE WoS citations from Altmetric scores. Conclusions: Scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because the focus of TE research importantly differs when the academic institutions and online platforms are compared, basic and clinical research groups, academic institutions, and health politicians should make a coordinated effort toward the design and implementation of adequate strategies for information diffusion and population health education. ",
doi="10.2196/25394",
url="https://www.jmir.org/2021/11/e25394",
url="http://www.ncbi.nlm.nih.gov/pubmed/34842548"
}


@Article{info:doi/10.2196/29176,
author="Greulich, Leonard
and Hegselmann, Stefan
and Dugas, Martin",
title="An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study",
journal="JMIR Med Inform",
year="2021",
month="Nov",
day="19",
volume="9",
number="11",
pages="e29176",
keywords="electronic data capture",
keywords="open science",
keywords="data interoperability",
keywords="metadata reuse",
keywords="mobile health",
keywords="data standard",
keywords="mobile phone",
abstract="Background: Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective: The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods: We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results: We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions: Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians. ",
doi="10.2196/29176",
url="https://medinform.jmir.org/2021/11/e29176",
url="http://www.ncbi.nlm.nih.gov/pubmed/34806987"
}


@Article{info:doi/10.2196/29146,
author="Chen, Wen
and Boggero, Andrea
and Del Puente, Giovanni
and Olcese, Martina
and Prestia, Davide
and Jahrami, Haitham
and Chalghaf, Nasr
and Guelmami, Noomen
and Azaiez, Fairouz
and Bragazzi, Luigi Nicola",
title="Googling for Suicide--Content and Quality Analysis of Suicide-Related Websites: Thematic Analysis",
journal="JMIR Form Res",
year="2021",
month="Nov",
day="11",
volume="5",
number="11",
pages="e29146",
keywords="suicide",
keywords="internet",
keywords="world wide web",
keywords="content analysis",
keywords="HONcode",
keywords="mental health",
keywords="webpage",
keywords="health information",
keywords="eHealth",
abstract="Background: Suicide represents a public health concern, imposing a dramatic burden. Prosuicide websites are ``virtual pathways'' facilitating a rise in suicidal behaviors, especially among socially isolated, susceptible individuals. Objective: The aim of this study is to characterize suicide-related webpages in the Italian language. Methods: The first 5 most commonly used search engines in Italy (ie, Bing, Virgilio, Yahoo, Google, and Libero) were mined using the term ``suicidio'' (Italian for suicide). For each search, the first 100 webpages were considered. Websites resulting from each search were collected and duplicates deleted so that unique webpages could be analyzed and rated with the HONcode instrument Results: A total of 65 webpages were included: 12.5\% (8/64) were antisuicide and 6.3\% (4/64) explicitly prosuicide. The majority of the included websites had a mixed or neutral attitude toward suicide (52/64, 81.2\%) and had informative content and purpose (39/64, 60.9\%). Most webpages targeted adolescents as an age group (38/64, 59.4\%), contained a reference to other psychiatric disorders or comorbidities (42/64, 65.6\%), included medical/professional supervision or guidance (45/64, 70.3\%), lacked figures or pictures related to suicide (41/64, 64.1\%), and did not contain any access restraint (62/64, 96.9\%). The major shortcoming to this study is the small sample size of webpages analyzed and the search limited to the keyword ``suicide.'' Conclusions: Specialized mental health professionals should try to improve their presence online by providing high-quality material. ",
doi="10.2196/29146",
url="https://formative.jmir.org/2021/11/e29146",
url="http://www.ncbi.nlm.nih.gov/pubmed/34689118"
}


@Article{info:doi/10.2196/25110,
author="Xie, Wenxiu
and Ji, Christine
and Hao, Tianyong
and Chow, Chi-Yin",
title="Predicting the Easiness and Complexity of English Health Materials for International Tertiary Students With Linguistically Enhanced Machine Learning Algorithms: Development and Validation Study",
journal="JMIR Med Inform",
year="2021",
month="Oct",
day="26",
volume="9",
number="10",
pages="e25110",
keywords="feature selection",
keywords="logistic regression",
keywords="online health resources",
abstract="Background: There is an increasing body of research on the development of machine learning algorithms in the evaluation of online health educational resources for specific readerships. Machine learning algorithms are known for their lack of interpretability compared with statistics. Given their high predictive precision, improving the interpretability of these algorithms can help increase their applicability and replicability in health educational research and applied linguistics, as well as in the development and review of new health education resources for effective and accessible health education. Objective: Our study aimed to develop a linguistically enriched machine learning model to predict binary outcomes of online English health educational resources in terms of their easiness and complexity for international tertiary students. Methods: Logistic regression emerged as the best performing algorithm compared with support vector machine (SVM) (linear), SVM (radial basis function), random forest, and extreme gradient boosting on the transformed data set using L2 normalization. We applied recursive feature elimination with SVM to perform automatic feature selection. The automatically selected features (n=67) were then further streamlined through expert review. The finalized feature set of 22 semantic features achieved a similar area under the curve, sensitivity, specificity, and accuracy compared with the initial (n=115) and automatically selected feature sets (n=67). Logistic regression with the linguistically enhanced feature set (n=22) exhibited important stability and robustness on the training data of different sizes (20\%, 40\%, 60\%, and 80\%), and showed consistently high performance when compared with the other 4 algorithms (SVM [linear], SVM [radial basis function], random forest, and extreme gradient boosting). Results: We identified semantic features (with positive regression coefficients) contributing to the prediction of easy-to-understand online health texts and semantic features (with negative regression coefficients) contributing to the prediction of hard-to-understand health materials for readers with nonnative English backgrounds. Language complexity was explained by lexical difficulty (rarity and medical terminology), verbs typical of medical discourse, and syntactic complexity. Language easiness of online health materials was associated with features such as common speech act verbs, personal pronouns, and familiar reasoning verbs. Successive permutation of features illustrated the interaction between these features and their impact on key performance indicators of the machine learning algorithms. Conclusions: The new logistic regression model developed exhibited consistency, scalability, and, more importantly, interpretability based on existing health and linguistic research. It was found that low and high linguistic accessibilities of online health materials were explained by 2 sets of distinct semantic features. This revealed the inherent complexity of effective health communication beyond current readability analyses, which were limited to syntactic complexity and lexical difficulty. ",
doi="10.2196/25110",
url="https://medinform.jmir.org/2021/10/e25110",
url="http://www.ncbi.nlm.nih.gov/pubmed/34698644"
}


@Article{info:doi/10.2196/28262,
author="Moon, C. Khatiya
and Van Meter, R. Anna
and Kirschenbaum, A. Michael
and Ali, Asra
and Kane, M. John
and Birnbaum, L. Michael",
title="Internet Search Activity of Young People With Mood Disorders Who Are Hospitalized for Suicidal Thoughts and Behaviors: Qualitative Study of Google Search Activity",
journal="JMIR Ment Health",
year="2021",
month="Oct",
day="22",
volume="8",
number="10",
pages="e28262",
keywords="suicide",
keywords="mood disorders",
keywords="depression",
keywords="internet",
keywords="search engine",
keywords="Google search",
keywords="digital health",
keywords="mobile health",
keywords="adolescent",
keywords="young adult",
abstract="Background: Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. Objective: We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health--related content. Methods: Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. Results: A majority (27/43, 63\%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21\% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77\%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44\% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44\% (19/43) of participants. Queries related to traumatic experiences were present among 33\% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). Conclusions: Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients. ",
doi="10.2196/28262",
url="https://mental.jmir.org/2021/10/e28262",
url="http://www.ncbi.nlm.nih.gov/pubmed/34677139"
}


@Article{info:doi/10.2196/30695,
author="Soltys, Coyle Frank
and Spilo, Kimi
and Politi, C. Mary",
title="The Content and Quality of Publicly Available Information About Congenital Diaphragmatic Hernia: Descriptive Study",
journal="JMIR Pediatr Parent",
year="2021",
month="Oct",
day="19",
volume="4",
number="4",
pages="e30695",
keywords="congenital diaphragmatic hernia",
keywords="prenatal counseling",
keywords="fetal care",
keywords="online information",
keywords="parental decision making",
abstract="Background: Congenital diaphragmatic hernia (CDH) diagnosis in an infant is distressing for parents. Parents often feel unable to absorb the complexities of CDH during prenatal consultations and use the internet to supplement their knowledge and decision making. Objective: We aimed to examine the content and quality of publicly available, internet-based CDH information. Methods: We conducted internet searches across 2 popular search engines (Google and Bing). Websites were included if they contained CDH information and were publicly available. We developed a coding instrument to evaluate websites. Two coders (FS and KS) were trained, achieved interrater reliability, and rated remaining websites independently. Descriptive statistics were performed. Results: Searches yielded 520 websites; 91 met inclusion criteria and were analyzed. Most websites provided basic CDH information including describing the defect (86/91, 95\%), need for neonatal intensive care (77/91, 85\%), and surgical correction (79/91, 87\%). Few mentioned palliative care, decisions about pregnancy termination (13/91, 14\%), or support resources (21/91, 23\%). Conclusions: Findings highlight the variability of information about CDH on the internet. Clinicians should work to develop or identify reliable, comprehensive information about CDH to support parents. ",
doi="10.2196/30695",
url="https://pediatrics.jmir.org/2021/4/e30695",
url="http://www.ncbi.nlm.nih.gov/pubmed/34665147"
}


@Article{info:doi/10.2196/24554,
author="Yuan, Kai
and Huang, Guangrui
and Wang, Lepeng
and Wang, Ting
and Liu, Wenbin
and Jiang, Haixu
and Yang, C. Albert",
title="Predicting Norovirus in the United States Using Google Trends: Infodemiology Study",
journal="J Med Internet Res",
year="2021",
month="Sep",
day="29",
volume="23",
number="9",
pages="e24554",
keywords="norovirus",
keywords="Google Trends",
keywords="correlation",
keywords="outbreak",
keywords="predictors",
abstract="Background: Norovirus is a contagious disease. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of internet search trends, internet search has been a useful tool to manifest infectious disease outbreaks. Objective: In this study, we tried to discover the correlation between internet search terms and norovirus infection. Methods: The internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and internet search terms in New York, California, and the United States as a whole. Results: Some Google search terms such as gastroenteritis, watery diarrhea, and stomach bug coincided with norovirus Google Trends. Some Google search terms such as contagious, travel, and party presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and coronavirus presented several months later than norovirus Google Trends. We found that fever, gastroenteritis, poison, cruise, wedding, and watery diarrhea were important factors correlated with norovirus Google Trends. In actual norovirus cases from New York, California, and the United States as a whole, some Google search terms presented with, earlier, or later than actual norovirus cases. Conclusions: Our study provides novel strategy-based internet search evidence regarding the epidemiology of norovirus. ",
doi="10.2196/24554",
url="https://www.jmir.org/2021/9/e24554",
url="http://www.ncbi.nlm.nih.gov/pubmed/34586079"
}


@Article{info:doi/10.2196/30161,
author="Teodoro, Douglas
and Ferdowsi, Sohrab
and Borissov, Nikolay
and Kashani, Elham
and Vicente Alvarez, David
and Copara, Jenny
and Gouareb, Racha
and Naderi, Nona
and Amini, Poorya",
title="Information Retrieval in an Infodemic: The Case of COVID-19 Publications",
journal="J Med Internet Res",
year="2021",
month="Sep",
day="17",
volume="23",
number="9",
pages="e30161",
keywords="information retrieval",
keywords="multistage retrieval",
keywords="neural search",
keywords="deep learning",
keywords="COVID-19",
keywords="coronavirus",
keywords="infodemic",
keywords="infodemiology",
keywords="literature",
keywords="online information",
abstract="Background: The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19--related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses. Objective: In the context of searching for scientific evidence in the deluge of COVID-19--related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language. Methods: Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents. Results: The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25--based baseline, retrieving on average, 83\% of relevant documents in the top 20. Conclusions: These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19--related questions posed using natural language. ",
doi="10.2196/30161",
url="https://www.jmir.org/2021/9/e30161",
url="http://www.ncbi.nlm.nih.gov/pubmed/34375298"
}


@Article{info:doi/10.2196/30401,
author="Abdelkader, Wael
and Navarro, Tamara
and Parrish, Rick
and Cotoi, Chris
and Germini, Federico
and Iorio, Alfonso
and Haynes, Brian R.
and Lokker, Cynthia",
title="Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review",
journal="JMIR Med Inform",
year="2021",
month="Sep",
day="9",
volume="9",
number="9",
pages="e30401",
keywords="machine learning",
keywords="bioinformatics",
keywords="information retrieval",
keywords="evidence-based medicine",
keywords="literature databases",
keywords="systematic review",
keywords="accuracy",
keywords="medical literature",
keywords="clinical support",
keywords="clinical care",
abstract="Background: The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. Objective: The goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. Methods: We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. Results: From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95\% while maintaining a high precision of 86\%. Conclusions: Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches. ",
doi="10.2196/30401",
url="https://medinform.jmir.org/2021/9/e30401",
url="http://www.ncbi.nlm.nih.gov/pubmed/34499041"
}


@Article{info:doi/10.2196/29175,
author="Ji, Meng
and Liu, Yanmeng
and Hao, Tianyong",
title="Predicting Health Material Accessibility: Development of Machine Learning Algorithms",
journal="JMIR Med Inform",
year="2021",
month="Sep",
day="1",
volume="9",
number="9",
pages="e29175",
keywords="health education materials",
keywords="Chinese language",
keywords="cognitive accessibility",
keywords="readability",
keywords="semantic features",
keywords="health education",
keywords="machine learning",
keywords="prediction",
keywords="accessibility",
keywords="health text",
keywords="cognition",
keywords="semantic",
keywords="algorithm",
keywords="health information",
abstract="Background: Current health information understandability research uses medical readability formulas to assess the cognitive difficulty of health education resources. This is based on an implicit assumption that medical domain knowledge represented by uncommon words or jargon form the sole barriers to health information access among the public. Our study challenged this by showing that, for readers from non-English speaking backgrounds with higher education attainment, semantic features of English health texts that underpin the knowledge structure of English health texts, rather than medical jargon, can explain the cognitive accessibility of health materials among readers with better understanding of English health terms yet limited exposure to English-based health education environments and traditions. Objective: Our study explores multidimensional semantic features for developing machine learning algorithms to predict the perceived level of cognitive accessibility of English health materials on health risks and diseases for young adults enrolled in Australian tertiary institutes. We compared algorithms to evaluate the cognitive accessibility of health information for nonnative English speakers with advanced education levels yet limited exposure to English health education environments. Methods: We used 113 semantic features to measure the content complexity and accessibility of original English resources. Using 1000 English health texts collected from Australian and international health organization websites rated by overseas tertiary students, we compared machine learning (decision tree, support vector machine [SVM], ensemble tree, and logistic regression) after hyperparameter optimization (grid search for the best hyperparameter combination of minimal classification errors). We applied 5-fold cross-validation on the whole data set for the model training and testing, and calculated the area under the operating characteristic curve (AUC), sensitivity, specificity, and accuracy as the measurement of the model performance. Results: We developed and compared 4 machine learning algorithms using multidimensional semantic features as predictors. The results showed that ensemble classifier (LogitBoost) outperformed in terms of AUC (0.858), sensitivity (0.787), specificity (0.813), and accuracy (0.802). Support vector machine (AUC 0.848, sensitivity 0.783, specificity 0.791, and accuracy 0.786) and decision tree (AUC 0.754, sensitivity 0.7174, specificity 0.7424, and accuracy 0.732) followed. Ensemble classifier (LogitBoost), support vector machine, and decision tree achieved statistically significant improvement over logistic regression in AUC, sensitivity, specificity, and accuracy. Support vector machine reached statistically significant improvement over decision tree in AUC and accuracy. As the best performing algorithm, ensemble classifier (LogitBoost) reached statistically significant improvement over decision tree in AUC, sensitivity, specificity, and accuracy. Conclusions: Our study shows that cognitive accessibility of English health texts is not limited to word length and sentence length as had been conventionally measured by medical readability formulas. We compared machine learning algorithms based on semantic features to explore the cognitive accessibility of health information for nonnative English speakers. The results showed the new models reached statistically increased AUC, sensitivity, and accuracy to predict health resource accessibility for the target readership. Our study illustrated that semantic features such as cognitive ability--related semantic features, communicative actions and processes, power relationships in health care settings, and lexical familiarity and diversity of health texts are large contributors to the comprehension of health information; for readers such as international students, semantic features of health texts outweigh syntax and domain knowledge. ",
doi="10.2196/29175",
url="https://medinform.jmir.org/2021/9/e29175",
url="http://www.ncbi.nlm.nih.gov/pubmed/34468321"
}


@Article{info:doi/10.2196/21817,
author="Worrall, P. Amy
and Kelly, Claire
and O'Neill, Aine
and O'Doherty, Murray
and Kelleher, Eoin
and Cushen, Marie Anne
and McNally, Cora
and McConkey, Samuel
and Glavey, Siobhan
and Lavin, Michelle
and de Barra, Eoghan",
title="Online Search Trends Influencing Anticoagulation in Patients With COVID-19: Observational Study",
journal="JMIR Form Res",
year="2021",
month="Aug",
day="31",
volume="5",
number="8",
pages="e21817",
keywords="COVID-19",
keywords="coronavirus",
keywords="online search engines",
keywords="anticoagulation",
keywords="thrombosis",
keywords="online influence",
keywords="health information dissemination",
abstract="Background: Early evidence of COVID-19--associated coagulopathy disseminated rapidly online during the first months of 2020, followed by clinical debate about how best to manage thrombotic risks in these patients. The rapid online spread of case reports was followed by online interim guidelines, discussions, and worldwide online searches for further information. The impact of global online search trends and online discussion on local approaches to coagulopathy in patients with COVID-19 has not been studied. Objective: The goal of this study was to investigate the relationship between online search trends using Google Trends and the rate of appropriate venous thromboembolism (VTE) prophylaxis and anticoagulation therapy in a cohort of patients with COVID-19 admitted to a tertiary hospital in Ireland. Methods: A retrospective audit of anticoagulation therapy and VTE prophylaxis among patients with COVID-19 who were admitted to a tertiary hospital was conducted between February 29 and May 31, 2020. Worldwide Google search trends of the term ``COVID-19'' and anticoagulation synonyms during this time period were determined and correlated against one another using a Spearman correlation. A P value of <.05 was considered significant, and analysis was completed using Prism, version 8 (GraphPad). Results: A statistically significant Spearman correlation (P<.001, r=0.71) was found between the two data sets, showing an increase in VTE prophylaxis in patients with COVID-19 with increasing online searches worldwide. This represents a proxy for online searches and discussion, dissemination of information, and Google search trends relating to COVID-19 and clotting risk, in particular, which correlated with an increasing trend of providing thromboprophylaxis and anticoagulation therapy to patients with COVID-19 in our tertiary center. Conclusions: We described a correlation of local change in clinical practice with worldwide online dialogue and digital search trends that influenced individual clinicians, prior to the publication of formal guidelines or a local quality-improvement intervention. ",
doi="10.2196/21817",
url="https://formative.jmir.org/2021/8/e21817",
url="http://www.ncbi.nlm.nih.gov/pubmed/34292865"
}


@Article{info:doi/10.2196/23367,
author="Lange-Drenth, Lukas
and Schulz, Holger
and Endsin, Gero
and Bleich, Christiane",
title="Patients With Cancer Searching for Cancer- or Health-Specific Web-Based Information: Performance Test Analysis",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="16",
volume="23",
number="8",
pages="e23367",
keywords="telemedicine",
keywords="eHealth",
keywords="eHealth literacy",
keywords="digital literacy",
keywords="internet",
keywords="web-based",
keywords="health information",
keywords="health education",
keywords="cancer",
keywords="mobile phone",
abstract="Background: Searching the internet for cancer-related information helps patients with cancer satisfy their unmet information needs and empowers them to play a more active role in the management of their disease. However, to benefit from the search, patients need a sufficient level of skill to search, select, appraise, and apply web-based health information. Objective: We aim to study the operational, navigational, information, and evaluation skills and problems of patients with cancer performing cancer-related search tasks using the internet. Methods: A total of 21 patients with cancer were recruited during their stay at the rehabilitation clinic for oncological rehabilitation. Participants performed eight cancer-related search tasks using the internet. The participants were asked to think aloud while performing the tasks, and the screen activities were recorded. The types and frequencies of performance problems were identified and coded into categories following an inductive coding process. In addition, the performance and strategic characteristics of task execution were summarized descriptively. Results: All participants experienced problems or difficulties in executing the tasks, and a substantial percentage of tasks (57/142, 40.1\%) could not be completed successfully. The participants' performance problems were coded into four categories, namely operating the computer and web browser, navigating and orientating, using search strategies, and evaluating the relevance and reliability of web-based information. The most frequent problems occurred in the third and fourth categories. A total of 90\% (19/21) of participants used nontask-related search terms or nonspecific search terms. A total of 95\% (20/21) of participants did not control for the source or topicality of the information found. In addition, none of the participants verified the information on 1 website with that on another website for each task. Conclusions: A substantial group of patients with cancer did not have the necessary skills to benefit from cancer-related internet searches. Future interventions are needed to support patients in the development of sufficient internet-searching skills, focusing particularly on information and evaluation skills. ",
doi="10.2196/23367",
url="https://www.jmir.org/2021/8/e23367",
url="http://www.ncbi.nlm.nih.gov/pubmed/34398801"
}


@Article{info:doi/10.2196/27477,
author="Col{\'o}n-Morales, M. Coralys
and Giang, W. Wayne C.
and Alvarado, Michelle",
title="Informed Decision-making for Health Insurance Enrollment: Survey Study",
journal="JMIR Form Res",
year="2021",
month="Aug",
day="12",
volume="5",
number="8",
pages="e27477",
keywords="health insurance",
keywords="information",
keywords="sources",
keywords="survey",
keywords="literacy",
abstract="Background: Health insurance enrollment is a difficult financial decision with large health impacts. Challenges such as low health insurance literacy and lack of knowledge about choosing a plan further complicate this decision-making process. Therefore, to support consumers in their choice of a health insurance plan, it is essential to understand how individuals go about making this decision. Objective: This study aims to understand the sources of information used by individuals to support their employer-provided health insurance enrollment decisions. It seeks to describe how individual descriptive factors lead to choosing a particular type of information source. Methods: An introduction was presented on health insurance plan selection and the sources of information used to support these decisions from the 1980s to the present. Subsequently, an electronic survey of 151 full-time faculty and staff members was conducted. The survey consisted of four sections: demographics, sources of information, health insurance literacy, and technology acceptance. Descriptive statistics were used to show the demographic characteristics of the 126 eligible respondents and to study the response behaviors in the remaining survey sections. Proportion data analysis was performed using the Cochran-Armitage trend test to understand the strength of the association between our variables and the types of sources used by the respondents. Results: In terms of demographics, most of the respondents were women (103/126, 81.7\%), represented a small household (1-2 persons; 87/126, 69\%), and used their insurance 3-12 times a year (52/126, 41.3\%). They assessed themselves as having moderate to high health insurance literacy and high acceptance of technology. The most selected and top-ranked sources were Official employer or state websites and Official Human Resources Virtual Benefits Counselor Alex. From our data analysis, we found that the use of official primary sources was constant across age groups and health insurance use groups. Meanwhile, the use of friends or family as a primary source slightly decreased as age and use increased. Conclusions: In this exploratory study, we identified the main sources of health insurance information among full-time employees from a large state university and found that most of the respondents needed 2-3 sources to gather all the information that they desired. We also studied and identified the relationships between individual factors (such as age, gender, and literacy) and 2 dependent variables on the types of primary sources of information. We encountered several limitations, which will be addressed in future studies. ",
doi="10.2196/27477",
url="https://formative.jmir.org/2021/8/e27477",
url="http://www.ncbi.nlm.nih.gov/pubmed/34387555"
}


@Article{info:doi/10.2196/25670,
author="He, Kai
and Yao, Lixia
and Zhang, JiaWei
and Li, Yufei
and Li, Chen",
title="Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="4",
volume="23",
number="8",
pages="e25670",
keywords="genealogical knowledge graph",
keywords="EHR",
keywords="information extraction",
keywords="genealogy",
keywords="neural network",
abstract="Background: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees. Objective: Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence. Methods: Built on a predefined family relationship map consisting of 4 types of entities (eg, people's name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries. Results: Our system achieved satisfying precision (94.79\%), recall (91.45\%), and F-1 measures (93.09\%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people. Conclusions: In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection. ",
doi="10.2196/25670",
url="https://www.jmir.org/2021/8/e25670",
url="http://www.ncbi.nlm.nih.gov/pubmed/34346903"
}


@Article{info:doi/10.2196/27539,
author="Choi, Hanna
and Jeong, Gyeonghui",
title="Characteristics of the Measurement Tools for Assessing Health Information--Seeking Behaviors in Nationally Representative Surveys: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="26",
volume="23",
number="7",
pages="e27539",
keywords="information seeking behavior",
keywords="consumer health information",
keywords="medical informatics",
keywords="health care surveys",
keywords="health information--seeking behavior",
keywords="surveys",
abstract="Background: The coronavirus pandemic (COVID-19) has also emerged as an infodemic, thereby worsening the harm of the pandemic. This situation has highlighted the need for a deeply rooted understanding of the health information--seeking behaviors (HISBs) of people. Objective: The aim of this paper was to review and provide insight regarding methodologies and the construct of content in HISB surveys by answering the following research question: what are the characteristics of the measurement tools for assessing HISBs in nationally representative surveys around the world? Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used as the framework for this study. A data search was performed through 5 international and 2 Korean databases covering the years between 2008 and 2020. Initially, studies performed among nationally representative samples were included to discover HISB survey instruments. The methodologies of the studies using HISB surveys were analyzed. For content analysis, 2 researchers reached a consensus through discussion by scrutinizing the contents of each survey questionnaire. Results: A total of 13 survey tools from 8 countries were identified after a review of 2333 records from the search results. Five survey tools (Health Information National Trends Survey, Health Tracking Survey, Annenberg National Health Communication Survey, National Health Interview Survey, and Health Tracking Household Survey) from the United States, 2 instruments from Germany, and 1 tool from each of the countries of the European Union, France, Israel, Poland, South Korea, and Taiwan were identified. Telephone or web-based surveys were commonly used targeting the adult population (?15 years of age). From the content analysis, the domains of the survey items were categorized as follows: information (information about health and patient medical records), channel (offline and online), and health (overall health, lifestyle, and cancer). All categories encompassed behavioral and attitude dimensions. A theoretical framework, that is, an information-channel-health structure for HISBs was proposed. Conclusions: The results of our study can contribute to the development and implementation of the survey tools for HISB with integrated questionnaire items. This will help in understanding HISB trends in national health care. ",
doi="10.2196/27539",
url="https://www.jmir.org/2021/7/e27539",
url="http://www.ncbi.nlm.nih.gov/pubmed/34309573"
}


@Article{info:doi/10.2196/23829,
author="Yechiam, Eldad
and Yom-Tov, Elad",
title="Unique Internet Search Strategies of Individuals With Self-Stated Autism: Quantitative Analysis of Search Engine Users' Investigative Behaviors",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="6",
volume="23",
number="7",
pages="e23829",
keywords="autism",
keywords="decision making",
keywords="exploration",
keywords="search",
keywords="internet",
abstract="Background: Although autism is often characterized in literature by the presence of repetitive behavior, in structured decision tasks, individuals with autism spectrum disorder (ASD) have been found to examine more options in a given time period than controls. Objective: We aimed to examine whether this investigative tendency emerges in information searches conducted via the internet. Methods: In total, 1746 search engine users stated that they had ASD in 2019. This group's naturally occurring responses following 1491 unique general queries and 78 image queries were compared to those of all other users of the search engine. The main dependent measure was scrolled distance, which denoted the extent to which additional results were scanned beyond the initial results presented on-screen. Additionally, we examined the number of clicks on search results as an indicator of the degree of search outcome exploitation and assessed whether there was a trade-off between increased search range and the time invested in viewing initial search results. Results: After issuing general queries, individuals with self-stated ASD scanned more results than controls. The scrolled distance in the results page of general queries was 45\% larger for the group of individuals with ASD (P<.001; d=0.45). The group of individuals with ASD also made the first scroll faster than the controls (P<.001; d=0.51). The differences in scrolled distance were larger for popular queries. No group differences in scrolled distance emerged for image queries, suggesting that visual load impeded the investigative behavior of individuals with ASD. No differences emerged in the number of clicks on search results. Conclusions: Individuals who self-stated that they had ASD scrutinized more general search results and fewer image search results than the controls. Thus, our results at least partially support the notion that individuals with ASD exhibit investigative behaviors and suggest that textual searches are an important context for expressing such tendencies. ",
doi="10.2196/23829",
url="https://www.jmir.org/2021/7/e23829",
url="http://www.ncbi.nlm.nih.gov/pubmed/34255644"
}


@Article{info:doi/10.2196/21680,
author="Mirzaei, Ardalan
and Aslani, Parisa
and Luca, Joseph Edward
and Schneider, Richard Carl",
title="Predictors of Health Information--Seeking Behavior: Systematic Literature Review and Network Analysis",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="2",
volume="23",
number="7",
pages="e21680",
keywords="information seeking",
keywords="network analysis",
keywords="health",
keywords="review",
keywords="temporal analysis",
keywords="mobile phone",
abstract="Background: People engage in health information--seeking behavior to support health outcomes, and being able to predict such behavior can inform the development of interventions to guide effective health information seeking. Obtaining a comprehensive list of the predictors of health information--seeking behavior through a systematic search of the literature and exploring the interrelationship of these predictors are critical first steps in this process. Objective: This study aims to identify significant predictors of health information--seeking behavior in the primary literature, develop a common taxonomy for these predictors, and identify the evolution of the concerned research field. Methods: A systematic search of PsycINFO, Scopus, and PubMed was conducted for all years up to and including December 10, 2019. Quantitative studies identifying significant predictors of health information--seeking behavior were included. Information seeking was broadly defined and not restricted to any source of health information. Data extraction of significant predictors was performed by 2 authors, and network analysis was conducted to observe the relationships between predictors with time. Results: A total of 9549 articles were retrieved, and after the screening, 344 studies were retained for analysis. A total of 1595 significant predictors were identified. These predictors were categorized into 67 predictor categories, with the most central predictors being age, education, gender, health condition, and financial income. With time, the interrelationship of predictors in the network became denser, with the growth of new predictor grouping reaching saturation (1 new predictor identified) in the past 7 years, despite increasing publication rates. Conclusions: A common taxonomy was developed to classify 67 significant predictors of health information--seeking behavior. A time-aggregated network method was developed to track the evolution of the research field, showing the maturation of new predictor terms and an increase in primary studies reporting multiple significant predictors of health information--seeking behavior. The literature has evolved with a decreased characterization of novel predictors of health information--seeking behavior. In contrast, we identified a parallel increase in the complexity of predicting health information--seeking behavior, with an increase in the literature describing multiple significant predictors. ",
doi="10.2196/21680",
url="https://www.jmir.org/2021/7/e21680",
url="http://www.ncbi.nlm.nih.gov/pubmed/33979776"
}


@Article{info:doi/10.2196/28272,
author="Liu, Zhiqiang
and Feng, Jingkun
and Yang, Zhihao
and Wang, Lei",
title="Document Retrieval for Precision Medicine Using a Deep Learning Ensemble Method",
journal="JMIR Med Inform",
year="2021",
month="Jun",
day="29",
volume="9",
number="6",
pages="e28272",
keywords="biomedical information retrieval",
keywords="document ranking",
keywords="precision medicine",
keywords="deep learning",
abstract="Background: With the development of biomedicine, the number of biomedical documents has increased rapidly bringing a great challenge for researchers trying to retrieve the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in specific retrieval tasks, thereby increasing the difficulty of biomedical information retrieval. Objective: This study aimed to find a more systematic method for retrieving relevant scientific literature for a given patient. Methods: In the initial retrieval stage, we supplemented query terms through query expansion strategies and applied query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employed a text classification model and relevance matching model to evaluate documents from different dimensions and then combined the outputs through logistic regression to re-rank all the documents from the initial ranking list. Results: The proposed ensemble method contributed to the improvement of biomedical retrieval performance. Compared with the existing deep learning--based methods, experimental results showed that our method achieved state-of-the-art performance on the data collection provided by the Text Retrieval Conference 2019 Precision Medicine Track. Conclusions: In this paper, we proposed a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and relevance matching model better captured semantic context information and improved retrieval performance. ",
doi="10.2196/28272",
url="https://medinform.jmir.org/2021/6/e28272",
url="http://www.ncbi.nlm.nih.gov/pubmed/34185006"
}


@Article{info:doi/10.2196/25868,
author="Hermes-DeSantis, R. Evelyn
and Hunter, T. Robert
and Welch, Julie
and Bhavsar, Roma
and Boulos, Daniel
and Noue, Marie-Ange",
title="Preferences for Accessing Medical Information in the Digital Age: Health Care Professional Survey",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="19",
volume="23",
number="6",
pages="e25868",
keywords="information-seeking behavior",
keywords="access to information",
keywords="internet",
keywords="physicians",
keywords="nurses",
keywords="pharmacists",
keywords="medical literature",
keywords="databases",
keywords="search tools",
keywords="medical information",
abstract="Background: Health care professionals (HCPs) routinely have questions concerning the medications they are recommending. There are numerous resources available; however, each has its own advantages and disadvantages. Objective: The purpose of this survey was to gain knowledge of the preferred methods and sources HCPs use to obtain information concerning medications. Methods: A total of 511 HCPs (202 physicians, 105 pharmacists, 100 advance practice nurses, 53 registered nurses, and 51 physician assistants) were surveyed through a third-party market research firm. All participants were practicing in the United States. Individuals working for a pharmaceutical company were excluded. The survey collected demographics, frequency of searching medical information, types of questions searched, sources of medical information, and rationale for preferred and nonpreferred sources of medical information. Use of medical information resources were rated on a 5-point ordinal scale. Data were analyzed with descriptive statistics. Results: Of the 511 respondents, 88.5\% (452/511) searched for medical information either daily or several times per week. The most common questions involved dosing and administration, drug-drug interactions, adverse events and safety, clinical practice guidelines, and disease state information. The main rationale for using specific medical websites or apps and general online search engines frequently or very frequently was ease of use (medical websites or apps: 269/356, 75.6\%; general online search engines: 248/284, 87.3\%). Accuracy was the main rationale for frequent or very frequent use of medical literature search databases (163/245, 66.5\%), prescribing labels or information (122/213, 57.3\%), and professional literature (120/195, 61.5\%). The main reason for rarely or never using specific medical websites or apps and medical literature search databases was unfamiliarity (medical websites or apps: 16/48, 33\%; medical literature search databases: 35/78, 45\%); for general online search engines, inaccuracy (34/54, 63\%); and for prescribing labels or information and professional literature, excessive time (prescribing labels or information : 54/102, 52.9\%; professional literature: 66/106, 62.3\%). The pharmaceutical company was sometimes used as a resource for medical information. When the medical information department was used, the call center and the website were considered thorough and complete (call center: 14/25, 56\%; website: 33/55, 60\%). However, the rationale for not using the call center was the time required (199/346, 57.5\%) and the website being unfamiliar (129/267, 48.3\%). Conclusions: The driving forces in the selection of resources are accuracy and ease of use. There is an opportunity to increase awareness of all the appropriate resources for HCPs which may aid in their daily clinical decisions. Specifically, pharmaceutical company medical information departments can help fulfill this need by addressing two major challenges with use of the pharmaceutical company: lack of awareness of medical information services and the speed at which responses are disseminated. Overall, there is lack of understanding or appreciation of the range of pathways to obtain published information and knowledge from pharmaceutical company medical information services. Among the many challenges resource champions will face are the ability to effectively make resources and their platforms accessible, known, and useful to the scientific community. ",
doi="10.2196/25868",
url="https://www.jmir.org/2021/6/e25868/",
url="http://www.ncbi.nlm.nih.gov/pubmed/36260374"
}


@Article{info:doi/10.2196/23293,
author="Jamal, Amr
and Tharkar, Shabana
and Alenazi, Hanan
and Julaidan, Saud Bedoor
and Al Hindawi, Ali Dania
and AlAkeel, Suleman Norah
and AlNuhayer, Mohammed Ola
and AlDubaikhi, Hamoud Raneem",
title="Usability Analysis of a Health Sciences Digital Library by Medical Residents: Cross-sectional Survey",
journal="JMIR Form Res",
year="2021",
month="Jun",
day="24",
volume="5",
number="6",
pages="e23293",
keywords="digital library usability",
keywords="medical education",
keywords="system usability scale",
keywords="medical residents",
keywords="Saudi Arabia",
abstract="Background: The usability of a digital library depends on a myriad of factors ranging from the end users' ability to website complexity. Although digital libraries provide instant access to online content, offering an efficient reference platform, their usability is highly variable. Objective: The aim of this study was to measure users' perspectives and usability of the digital library of the Saudi Commission for Health Specialties (SCFHS). Methods: A web-based questionnaire survey was conducted using a validated System Usability Scale (SUS) containing 5 positive and 5 negative items on the usability of the digital library. The SUS standard cut-off score of 68 was considered for interpretation. Results: The overall mean SUS score of digital library usability was 52.9 (SD 15.2) with a grade ``D'' categorization, indicating low usability. The perceived measures of attributes of the 10 SUS items of findability, complexity, consistency, and confidence obtained below average scores. Only item 1 relating to perceived willingness to use the digital library frequently obtained a score above the targeted benchmark score (mean score 3.6). Higher SUS scores were associated with training (P=.02). Men felt the digital library to be more complex (P=.04) and board-certified physicians perceived a greater need for training on digital library use (P=.05). Only the UpToDate database was widely used (72/90, 80\%). Conclusions: These findings demonstrate the low usability of the extensive facilities offered by the SCFHS digital library. It is pivotal to improve awareness of the availability of the digital library and popularize the databases. There is also a need for improved user training to enhance the accessibility and usability of the multiple databases. ",
doi="10.2196/23293",
url="https://formative.jmir.org/2021/6/e23293/",
url="http://www.ncbi.nlm.nih.gov/pubmed/34184992"
}


@Article{info:doi/10.2196/26892,
author="Deng, Lizong
and Chen, Luming
and Yang, Tao
and Liu, Mi
and Li, Shicheng
and Jiang, Taijiao",
title="Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="15",
volume="23",
number="6",
pages="e26892",
keywords="knowledge graph",
keywords="knowledge granularity",
keywords="machine learning",
keywords="high-fidelity phenotyping",
keywords="phenotyping",
keywords="phenotype",
keywords="semantic",
abstract="Background: Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associated with these phenotypes. Objective: To characterize the details of disease phenotypes for clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (semantic structured unit of phenotypes). Methods: PhenoSSU is an ``entity-attribute-value'' model by its very nature, and it aims to capture the full semantic information underlying phenotype descriptions with a series of attributes and values. A total of 193 clinical guidelines for infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on the co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether PhenoSSU instances could capture the full semantics underlying the descriptions of the corresponding phenotypes. To automatically construct fine-grained phenotype knowledge graphs, a hybrid strategy that first recognized phenotype concepts with the MetaMap tool and then predicted the attribute values of phenotypes with machine learning classifiers was developed. Results: Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. A total of 4020 PhenoSSU instances were annotated in these knowledge graphs, and 3757 of them (89.5\%) were found to be able to capture the full semantics underlying the descriptions of the corresponding phenotypes listed in clinical guidelines. By comparison, other information models, such as the clinical element model and the HL7 fast health care interoperability resource model, could only capture the full semantics underlying 48.4\% (2034/4020) and 21.8\% (914/4020) of the descriptions of phenotypes listed in clinical guidelines, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. Conclusions: PhenoSSU is an effective information model for the precise representation of phenotype knowledge for clinical guidelines, and machine learning can be used to improve the efficiency of constructing PhenoSSU-based knowledge graphs. Our work will potentially shift the focus of medical knowledge engineering from a coarse-grained level to a more fine-grained level. ",
doi="10.2196/26892",
url="https://www.jmir.org/2021/6/e26892",
url="http://www.ncbi.nlm.nih.gov/pubmed/34128811"
}


@Article{info:doi/10.2196/25236,
author="Kolotylo-Kulkarni, Malgorzata
and Seale, E. Deborah
and LeRouge, M. Cynthia",
title="Personal Health Information Management Among Older Adults: Scoping Review",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="7",
volume="23",
number="6",
pages="e25236",
keywords="personal health information management",
keywords="health information management",
keywords="scoping review",
keywords="information management",
keywords="consumer health informatics",
keywords="medical informatics",
keywords="patient participation",
abstract="Background: Older adults face growing health care needs and could potentially benefit from personal health information management (PHIM) and PHIM technology. To ensure effective PHIM and to provide supportive tools, it is crucial to investigate the needs, challenges, processes, and tools used by this subpopulation. The literature on PHIM by older adults, however, remains scattered and has not provided a clear picture of what we know about the elements that play a role in older adults' PHIM. Objective: The goal of our review was to provide a comprehensive overview of extant knowledge on PHIM by older adults, establish the status quo of research on this topic, and identify research gaps. Methods: We carried out a scoping review of the literature from 1998 to 2020, which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework. First, we executed a broad and structured search. We then carried out a qualitative analysis of papers pertinent to the topic taking into consideration the five elements of the patient work system as follows: (1) personal-level factors, (2) PHIM tasks, (3) tools used, (4) physical settings of PHIM activities, and (5) socio-organizational aspects. Results: The review included 22 studies. Consolidated empirical evidence was related to all elements of the patient work system. Multiple personal factors affected PHIM. Various types of personal health information were managed (clinical, patient-generated, and general) and tools were used (electronic, paper-based, and others). Older adults' PHIM was intertwined with their surroundings, and various individuals participated. The largest body of evidence concerned personal factors, while findings regarding the physical environment of PHIM were scarce. Most research has thus far examined older adults as a single group, and scant attention has been paid to age subgroups. Conclusions: Opportunities for further PHIM studies remain across all elements of the patient work system in terms of empirical, design science, or review work. ",
doi="10.2196/25236",
url="https://www.jmir.org/2021/6/e25236",
url="http://www.ncbi.nlm.nih.gov/pubmed/34096872"
}


@Article{info:doi/10.2196/29507,
author="Rasmussen, Neimann Lauge
and Norgaard, Ole
and Andersen, Helms Tue
and Palayew, Adam
and Nicholson, Joey
and Lazarus, V. Jeffrey",
title="Authors' Reply to: Redundancy of Terms in Search Strategies. Comment on ``Searching PubMed to Retrieve Publications on the COVID-19 Pandemic: Comparative Analysis of Search Strings''",
journal="J Med Internet Res",
year="2021",
month="May",
day="28",
volume="23",
number="5",
pages="e29507",
keywords="coronavirus",
keywords="COVID-19",
keywords="pandemic",
keywords="scientific publishing",
keywords="PubMed",
keywords="literature searching",
keywords="research",
keywords="literature",
keywords="search",
keywords="performance",
keywords="search strategy",
doi="10.2196/29507",
url="https://www.jmir.org/2021/5/e29507",
url="http://www.ncbi.nlm.nih.gov/pubmed/33989168"
}


@Article{info:doi/10.2196/28666,
author="Campos, Oliveira Daniel Melo De
and Fulco, Laino Umberto
and Oliveira, Nobre Jonas Ivan",
title="Redundancy of Terms in Search Strategies. Comment on ``Searching PubMed to Retrieve Publications on the COVID-19 Pandemic: Comparative Analysis of Search Strings''",
journal="J Med Internet Res",
year="2021",
month="May",
day="28",
volume="23",
number="5",
pages="e28666",
keywords="coronavirus",
keywords="COVID-19",
keywords="pandemic",
keywords="scientific publishing",
keywords="PubMed",
keywords="literature searching",
keywords="research",
keywords="literature",
keywords="search",
keywords="performance",
keywords="search strategy",
doi="10.2196/28666",
url="https://www.jmir.org/2021/5/e28666",
url="http://www.ncbi.nlm.nih.gov/pubmed/33989165"
}


@Article{info:doi/10.2196/27712,
author="Sivesind, Elise Torunn
and Szeto, D. Mindy
and Kim, William
and Dellavalle, Paul Robert",
title="Google Trends in Dermatology: Scoping Review of the Literature",
journal="JMIR Dermatol",
year="2021",
month="May",
day="25",
volume="4",
number="1",
pages="e27712",
keywords="Google Trends",
keywords="search trends",
keywords="internet",
keywords="infodemiology",
keywords="infoveillance",
keywords="search terms",
keywords="dermatology",
keywords="skin cancer",
keywords="databases",
abstract="Background: Google Trends is a powerful online database and analytics tool of popular Google search queries over time and has the potential to inform medical practice and priorities. Objective: This review aimed to survey Google Trends literature in dermatology and elucidate its current roles and relationships with the field. Methods: A literature search was performed using PubMed to access and review relevant dermatology-related Google Trends studies published within the last 5 years. Results: Current research utilizing Google Trends data provides insight related to skin cancer, pruritus, cosmetic procedures, and COVID-19. We also found that dermatology is presently the highest-searched medical specialty---among 15 medical and surgical specialties as well as general practitioners. Google searches related to dermatology demonstrate a seasonal nature for various skin conditions and sun-related topics, depending on a region's inherent climate and hemi-sphere. In addition, celebrity social media and other viral posts have been found to potentiate Google searches about dermatology and drive public interest. Conclusions: A limited number of relevant studies may have been omitted by the simplified search strategy of this study, as well as by restriction to English language articles and articles indexed in the PubMed database. This could be expanded upon in a secondary systematic review. Future re-search is warranted to better understand how Google Trends can be utilized to improve the quality of clinic visits, drive public health campaigns, and detect disease clusters in real time. ",
doi="10.2196/27712",
url="https://derma.jmir.org/2021/1/e27712",
url="http://www.ncbi.nlm.nih.gov/pubmed/37632813"
}


@Article{info:doi/10.2196/18593,
author="Hswen, Yulin
and Zhang, Amanda
and Ventelou, Bruno",
title="Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis",
journal="JMIR Public Health Surveill",
year="2021",
month="May",
day="10",
volume="7",
number="5",
pages="e18593",
keywords="digital epidemiology",
keywords="Google queries",
keywords="asthma",
keywords="symptoms",
keywords="health information seeking",
abstract="Background: Asthma affects over 330 million people worldwide. Timing of an asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms. Objective: This study evaluates the utility of the internet search query data for the identification of the onset of asthma symptoms. Methods: Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks --1 and --2 as exogenous variables were conducted to validate our correlation results. Results: Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04). Conclusions: Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset. ",
doi="10.2196/18593",
url="https://publichealth.jmir.org/2021/5/e18593",
url="http://www.ncbi.nlm.nih.gov/pubmed/33970108"
}


@Article{info:doi/10.2196/22986,
author="Threats, Megan
and Bond, Keosha",
title="HIV Information Acquisition and Use Among Young Black Men Who Have Sex With Men Who Use the Internet: Mixed Methods Study",
journal="J Med Internet Res",
year="2021",
month="May",
day="7",
volume="23",
number="5",
pages="e22986",
keywords="HIV",
keywords="health information behavior",
keywords="eHealth",
keywords="mHealth social media",
keywords="consumer health informatics",
keywords="mobile phones",
keywords="sexual and gender minorities",
keywords="African Americans",
keywords="young adults",
keywords="mixed methods",
abstract="Background: HIV disproportionately affects young Black men who have sex with men (YBMSM) in the United States. eHealth holds potential for supporting linkage and engagement in HIV prevention and care and the delivery of HIV information to YBMSM. Objective: This study aims to investigate HIV information acquisition and use among YBMSM who use the internet. Methods: A web-based self-administered survey and semistructured interviews were conducted. The survey findings informed the development of the interview guide. Descriptive statistics were used to characterize the survey sample, and interview data were analyzed thematically using modified grounded theory methodologies. Results: Among the internet sample (N=83), the average age was 29.2 (SD 3.5) years, 41\% (n=34) of participants self-reported living with HIV, 43\% (n=36) were HIV-negative, and 15\% (n=13) were unsure of their HIV status. Most participants (n=79, 95\%) acquired HIV information through the internet while using a mobile phone. Web-based HIV information was intentionally sought from consumer health information websites (n=31, 37\%), government health information websites (n=25, 30\%), and social media (n=14, 17\%). Most men incidentally acquired HIV information via advertisements on social media sites and geospatial dating apps (n=54, 65\%), posts on social media sites from their web-based social ties (n=44, 53\%), and advertisements while browsing the internet (n=40, 48\%). Although the internet is the top source of HIV information, health care providers were the most preferred (n=42, 50\%) and trusted (n=80, 96\%) source of HIV information. HIV information was used to facilitate the use of HIV prevention and care services. The qualitative sample included YBMSM across a range of ages and at different points of engagement in HIV prevention and care. Qualitative findings included the importance of the internet as a primary source of HIV information. The internet was used because of its ease of accessibility, because of its ability to maintain anonymity while searching for sensitive information, and to mitigate intersecting stigmas in health care settings. Participants used HIV information to assess their risk for HIV and AIDS, support their skill building for HIV prevention, inform patient--doctor communication, and learn about HIV prevention and treatment options. Men expressed concerns about their diminishing access to online spaces for HIV information exchange among YBMSM because of censorship policies on social media sites and the stigmatizing framing and tone of mass media HIV-prevention advertisements encountered while using the internet. Conclusions: YBMSM in this sample had high utilization of eHealth for HIV information acquisition and use but diminished access to their preferred and most trusted source of HIV information: health care providers. Future eHealth-based HIV interventions culturally tailored for YBMSM should aim to reduce intersectional stigma at the point of care and support patient--provider communication. The findings demonstrate the need for community-informed, culturally tailored HIV messaging and online spaces for informational support exchange among YBMSM. ",
doi="10.2196/22986",
url="https://www.jmir.org/2021/5/e22986",
url="http://www.ncbi.nlm.nih.gov/pubmed/33960953"
}


@Article{info:doi/10.2196/22933,
author="Mangono, Tichakunda
and Smittenaar, Peter
and Caplan, Yael
and Huang, S. Vincent
and Sutermaster, Staci
and Kemp, Hannah
and Sgaier, K. Sema",
title="Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data",
journal="J Med Internet Res",
year="2021",
month="May",
day="3",
volume="23",
number="5",
pages="e22933",
keywords="Google Trends",
keywords="coronavirus",
keywords="COVID-19",
keywords="principal component analysis",
keywords="information-seeking trends",
keywords="information retrieval",
keywords="trend",
keywords="infodemiology",
keywords="infoveillance",
keywords="virus",
keywords="public health",
keywords="information seeking",
keywords="online health information",
abstract="Background: The COVID-19 pandemic has impacted people's lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with---or precede---real-life events? Methods: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. Results: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. Conclusions: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool. ",
doi="10.2196/22933",
url="https://www.jmir.org/2021/5/e22933",
url="http://www.ncbi.nlm.nih.gov/pubmed/33878015"
}


@Article{info:doi/10.2196/26331,
author="Chrzanowski, J?drzej
and So?ek, Julia
and Fendler, Wojciech
and Jemielniak, Dariusz",
title="Assessing Public Interest Based on Wikipedia's Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="12",
volume="23",
number="4",
pages="e26331",
keywords="COVID-19",
keywords="pandemic",
keywords="media",
keywords="Wikipedia",
keywords="internet",
keywords="online health information",
keywords="information seeking",
keywords="interest",
keywords="retrospective",
keywords="surveillance",
keywords="infodemiology",
keywords="infoveillance",
abstract="Background: In the current era of widespread access to the internet, we can monitor public interest in a topic via information-targeted web browsing. We sought to provide direct proof of the global population's altered use of Wikipedia medical knowledge resulting from the new COVID-19 pandemic and related global restrictions. Objective: We aimed to identify temporal search trends and quantify changes in access to Wikipedia Medicine Project articles that were related to the COVID-19 pandemic. Methods: We performed a retrospective analysis of medical articles across nine language versions of Wikipedia and country-specific statistics for registered COVID-19 deaths. The observed patterns were compared to a forecast model of Wikipedia use, which was trained on data from 2015 to 2019. The model comprehensively analyzed specific articles and similarities between access count data from before (ie, several years prior) and during the COVID-19 pandemic. Wikipedia articles that were linked to those directly associated with the pandemic were evaluated in terms of degrees of separation and analyzed to identify similarities in access counts. We assessed the correlation between article access counts and the number of diagnosed COVID-19 cases and deaths to identify factors that drove interest in these articles and shifts in public interest during the subsequent phases of the pandemic. Results: We observed a significant (P<.001) increase in the number of entries on Wikipedia medical articles during the pandemic period. The increased interest in COVID-19--related articles temporally correlated with the number of global COVID-19 deaths and consistently correlated with the number of region-specific COVID-19 deaths. Articles with low degrees of separation were significantly similar (P<.001) in terms of access patterns that were indicative of information-seeking patterns. Conclusions: The analysis of Wikipedia medical article popularity could be a viable method for epidemiologic surveillance, as it provides important information about the reasons behind public attention and factors that sustain public interest in the long term. Moreover, Wikipedia users can potentially be directed to credible and valuable information sources that are linked with the most prominent articles. ",
doi="10.2196/26331",
url="https://www.jmir.org/2021/4/e26331",
url="http://www.ncbi.nlm.nih.gov/pubmed/33667176"
}


@Article{info:doi/10.2196/22618,
author="Ghani, Shahi
and Fan, Siu Ka
and Fan, Hay Ka
and Lenti, Lorenzo
and Raptis, Dimitri",
title="Using the Ensuring Quality Information for Patients Tool to Assess Patient Information on Appendicitis Websites: Systematic Search and Evaluation",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="26",
volume="23",
number="3",
pages="e22618",
keywords="appendicitis",
keywords="patient information",
keywords="EQIP tool",
keywords="quality",
keywords="tool",
keywords="surgery",
keywords="online health information",
keywords="internet",
keywords="health-seeking",
keywords="behavior",
keywords="review",
abstract="Background: Appendicitis is a common surgical problem among the young adult population, who are likely to use the internet to obtain medical information. This information may determine the health-seeking behavior of an individual and may delay medical attention. Little is known regarding the quality of patient information on appendicitis on the internet, as this has not been previously studied. Objective: The aim of our study was to identify the quality of information regarding appendicitis on websites intended for the public. Methods: We conducted a systematic review of information on appendicitis available online using the following 4 search terms in google: ``appendicitis,'' ``appendix,'' ``appendectomy,'' and ``appendicectomy''. The top 100 websites of each search term were assessed using the validated Ensuring Quality Information for Patients (EQIP) tool (score 0-36). Results: A total of 119 websites met the eligibility criteria for evaluation. The overall median EQIP score for all websites was 20 (IQR 18-22). More than half the websites originated from the USA (65/119, 54.6\%), and 45.4\% (54/119) of all websites originated from hospitals, although 43\% (23/54) of these did not mention qualitative risks from surgery. Incidence rates were only provided for complications and mortality in 12.6\% (15/119) and 3.3\% (4/119) of all websites, respectively. Conclusions: The assessment of the quality and readability of websites concerning appendicitis by the EQIP tool indicates that most sites online were of poor credibility, with minimal information regarding complication rates and mortality. To improve education and awareness of appendicitis, there is an immediate need for more informative and patient-centered websites that are more compatible with international quality standards. ",
doi="10.2196/22618",
url="https://www.jmir.org/2021/3/e22618",
url="http://www.ncbi.nlm.nih.gov/pubmed/33729160"
}


@Article{info:doi/10.2196/21642,
author="Alasmari, Ashwag
and Zhou, Lina",
title="Share to Seek: The Effects of Disease Complexity on Health Information--Seeking Behavior",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="24",
volume="23",
number="3",
pages="e21642",
keywords="health information consumers",
keywords="multimorbidity",
keywords="information searching",
keywords="information seeking",
keywords="disease development",
abstract="Background: Web-based question and answer (Q\&A) sites have emerged as an alternative source for serving individuals' health information needs. Although a number of studies have analyzed user-generated content in web-based Q\&A sites, there is insufficient understanding of the effect of disease complexity on information-seeking needs and the types of information shared, and little research has been devoted to the questions concerning multimorbidity. Objective: This study aims to investigate seeking of health information in Q\&A sites at different levels of disease complexity. Specifically, this study investigates the effects of disease complexity on information-seeking needs, types of information shared, and stages of disease development. Methods: First, we selected a random sample of 400 questions separately from each of the Q\&A sites: Yahoo Answers and WebMD Answers. The data cleaning resulted in a final set of 624 questions from the two sites.  We used a mixed methods approach, including qualitative content analysis and quantitative statistical analysis. Results: The one-way results of ANOVA showed significant effects of disease complexity (single vs multimorbid disease questions) on two information-seeking needs: diagnosis (F1,622=5.08; P=.02) and treatment (F1,622=4.82; P=.02). There were also significant differences between the two levels of disease complexity in two stages of disease development: the general health stage (F1,622=48.02; P<.001) and the chronic stage (F1,622=54.01; P<.001). In addition, our results showed significant effects of disease complexity across all types of shared information: demographic information (F1,622=32.24; P<.001), medical diagnosis (F1,622=11.04; P<.001), and treatment and prevention (F1,622=14.55; P<.001). Conclusions: Our findings present implications for the design of web-based Q\&A sites to better support health information seeking. Future studies should be conducted to validate the generality of these findings and apply them to improve the effectiveness of  health information in Q\&A sites. ",
doi="10.2196/21642",
url="https://www.jmir.org/2021/3/e21642",
url="http://www.ncbi.nlm.nih.gov/pubmed/33759803"
}


@Article{info:doi/10.2196/22860,
author="Lu, Zhao-Hua
and Wang, Xiaoqing Jade
and Li, Xintong",
title="Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="19",
volume="23",
number="3",
pages="e22860",
keywords="natural language processing",
keywords="question-answering systems",
keywords="language summarization",
keywords="machine learning",
keywords="life and medical sciences",
keywords="COVID-19",
keywords="public health",
keywords="coronavirus literature",
abstract="Background: COVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive. Objective: A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. Methods: Given a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions. Results: We applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19--related questions. Conclusions: Compared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19--related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years. ",
doi="10.2196/22860",
url="https://www.jmir.org/2021/3/e22860",
url="http://www.ncbi.nlm.nih.gov/pubmed/33739287"
}


@Article{info:doi/10.2196/20030,
author="Chiu, Yen-Lin
and Lee, Yu-Chen
and Tsai, Chin-Chung",
title="Internet-Specific Epistemic Beliefs in Medicine and Intention to Use Evidence-Based Online Medical Databases Among Health Care Professionals: Cross-sectional Survey",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="18",
volume="23",
number="3",
pages="e20030",
keywords="evidence-based medicine (EBM)",
keywords="health care professionals",
keywords="internet-specific epistemic beliefs",
keywords="medical informatics",
abstract="Background: Evidence-based medicine has been regarded as a prerequisite for ensuring health care quality. The increase in health care professionals' adoption of web-based medical information and the lack of awareness of alternative access to evidence-based online resources suggest the need for an investigation of their information-searching behaviors of using evidence-based online medical databases. Objective: The main purposes of this study were to (1) modify and validate the internet-specific epistemic beliefs in medicine (ISEBM) questionnaire and (2) explore the associations between health care professionals' demographics, ISEBM, and intention to use evidence-based online medical databases for clinical practice. Methods: Health care professionals in a university-affiliated teaching hospital were surveyed using the ISEBM questionnaire. The partial least squares-structural equation modeling was conducted to analyze the reliability and validity of ISEBM. Furthermore, the structural model was analyzed to examine the possible linkages between health professionals' demographics, ISEBM, and intention to utilize the evidence-based online medical databases for clinical practice. Results: A total of 273 health care professionals with clinical working experience were surveyed. The results of the measurement model analysis indicated that all items had significant loadings ranging from 0.71 to 0.92 with satisfactory composite reliability values ranging from 0.87 to 0.94 and average variance explained values ranging from 0.70 to 0.84. The results of the structural relationship analysis revealed that the source of internet-based medical knowledge (path coefficient --0.26, P=.01) and justification of internet-based knowing in medicine (path coefficient 0.21, P=.001) were correlated with the intention to use evidence-based online medical databases. However, certainty and simplicity of internet-based medical knowledge were not. In addition, gender (path coefficient 0.12, P=.04) and academic degree (path coefficient 0.15, P=.004) were associated with intention to use evidence-based online medical databases for clinical practice. Conclusions: Advancing health care professionals' ISEBM regarding source and justification may encourage them to retrieve valid medical information through evidence-based medical databases. Moreover, providing support for specific health care professionals (ie, females, without a master's degree) may promote their intention to use certain databases for clinical practice. ",
doi="10.2196/20030",
url="https://www.jmir.org/2021/3/e20030",
url="http://www.ncbi.nlm.nih.gov/pubmed/33734092"
}


@Article{info:doi/10.2196/13182,
author="Martinez-Garcia, Alicia
and Naranjo-Saucedo, Bel{\'e}n Ana
and Rivas, Antonio Jose
and Romero Tabares, Antonio
and Mar{\'i}n Cassinello, Ana
and Andr{\'e}s-Mart{\'i}n, Anselmo
and S{\'a}nchez Laguna, Jos{\'e} Francisco
and Villegas, Roman
and P{\'e}rez Le{\'o}n, Paula Francisco De
and Moreno Conde, Jes{\'u}s
and Parra Calder{\'o}n, Luis Carlos",
title="A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="10",
volume="9",
number="3",
pages="e13182",
keywords="evidence-based medicine",
keywords="clinical decision support system",
keywords="scientific knowledge integration",
abstract="Background: The evidence-based medicine (EBM) paradigm requires the development of health care professionals' skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients' health care, increase patient safety, and optimize resources use. Objective: The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice. Methods: Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy. Results: The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times. Conclusions: The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future. ",
doi="10.2196/13182",
url="https://medinform.jmir.org/2021/3/e13182",
url="http://www.ncbi.nlm.nih.gov/pubmed/33709932"
}


@Article{info:doi/10.2196/24945,
author="Zhang, Di
and Shi, Zhen
and Hu, Hongchao
and Han, (Kevin) Gang",
title="Classification of the Use of Online Health Information Channels and Variation in Motivations for Channel Selection: Cross-sectional Survey",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="9",
volume="23",
number="3",
pages="e24945",
keywords="search",
keywords="browse",
keywords="scan",
keywords="health information seeking",
keywords="channel selection",
keywords="health information",
keywords="health education",
keywords="health communication",
keywords="online media",
abstract="Background: Existing health education and communication research routinely measures online channel use as a whole by, for example, evaluating how frequently people use the internet to search for health information. This approach fails to capture the complexity and diversity of online channel use in health information seeking. The measurement of generic online channel use may cause too much error, and it lends no support to media planning in public health promotion campaigns or scholarly research involving online channel use. Objective: This study intends to present a thorough picture of patterns of online health information channel use and classify the use of various types of online health information channels, including WeChat, microblogs, web portals, search engines, mobile apps, and online forums. Under the framework of the risk information seeking and processing model, this study also analyzes the differences in individuals' motivations for channel selection to offer further evidence to validate the classification scheme. Methods: This study sampled 542 Chinese internet users in Beijing. The average age of the respondents was 33 years, female respondents accounted for 52.0\% (282/542) of the sample, and the average monthly income ranged from US \$900 to \$1200. The study surveyed the use of 13 commonly used online health information channels and various sociopsychological factors associated with online health information seeking. Results: This study derived 3 categories of online health information channels: searching, browsing, and scanning channels. It was found that the use of online searching channels was affect driven ?(B=0.11; $\beta$=0.10; P=.02) and characterized by a stronger need for health knowledge ?(B=0.09; $\beta$=0.01; P<.001). The use of browsing channels was directly influenced by informational subjective norms ?(B=0.33; $\beta$=0.15; P=.004) and perceived current knowledge ?(B=0.007; $\beta$=0.09; P=.003). The use of scanning channels was mainly influenced by informational subjective norms ?(B=0.29; $\beta$=0.15; P=.007). Conclusions: The results of this study suggest that health communication practitioners and scholars may consider measuring the use of internet, new media, or online media more precisely instead of simply asking the public about the frequency of online channel use or internet use in the acquisition of health information. Scholars and practitioners may consider measuring the use of online health information channels by using the 3-category scheme described in this study. Future research is encouraged to further explore how people process health information when using different online channels. ",
doi="10.2196/24945",
url="https://www.jmir.org/2021/3/e24945",
url="http://www.ncbi.nlm.nih.gov/pubmed/33687342"
}


@Article{info:doi/10.2196/22645,
author="Kammrath Betancor, Paola
and Tizek, Linda
and Zink, Alexander
and Reinhard, Thomas
and B{\"o}hringer, Daniel",
title="Estimating the Incidence of Conjunctivitis by Comparing the Frequency of Google Search Terms With Clinical Data: Retrospective Study",
journal="JMIR Public Health Surveill",
year="2021",
month="Mar",
day="3",
volume="7",
number="3",
pages="e22645",
keywords="epidemic keratoconjunctivitis",
keywords="big data",
keywords="Google search",
keywords="Freiburg clinical data",
abstract="Background: Infectious conjunctivitis is contagious and may lead to an outbreak. Prevention systems can help to avoid an outbreak. Objective: We aimed to evaluate if Google search data on conjunctivitis and associated terms can be used to estimate the incidence and if the data can provide an estimation for outbreaks. Methods: We obtained Google search data over 4 years for the German term for conjunctivitis (``Bindehautentz{\"u}ndung'') and 714 associated terms in 12 selected German cities and Germany as a whole using the Google AdWords Keyword Planner. The search volume from Freiburg was correlated with clinical data from the Freiburg emergency practice (Eye Center University of Freiburg). Results: The search volume for the German term for conjunctivitis in Germany as a whole and in the 12 German cities showed a highly uniform seasonal pattern. Cross-correlation between the temporal search frequencies in Germany as a whole and the 12 selected cities was high without any lag. Cross-correlation of the search volume in Freiburg with the frequency of conjunctivitis (International Statistical Classification of Diseases and Related Health Problems [ICD] code group ``H10.-'') from the centralized ophthalmologic emergency practice in Freiburg revealed a considerable temporal association, with the emergency practice lagging behind the frequency. Additionally, Pearson correlation between the count of patients per month and the count of searches per month in Freiburg was statistically significant (P=.04). Conclusions: We observed a close correlation between the Google search volume for the signs and symptoms of conjunctivitis and the frequency of patients with a congruent diagnosis in the Freiburg region. Regional deviations from the nationwide average search volume may therefore indicate a regional outbreak of infectious conjunctivitis. ",
doi="10.2196/22645",
url="https://publichealth.jmir.org/2021/3/e22645",
url="http://www.ncbi.nlm.nih.gov/pubmed/33656450"
}


@Article{info:doi/10.2196/22505,
author="Inau, Thea Esther
and Sack, Jean
and Waltemath, Dagmar
and Zeleke, Alamirrew Atinkut",
title="Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review",
journal="JMIR Res Protoc",
year="2021",
month="Feb",
day="2",
volume="10",
number="2",
pages="e22505",
keywords="data stewardship",
keywords="FAIR data principles",
keywords="health research",
keywords="PRISMA",
keywords="scoping review",
abstract="Background: Data stewardship is an essential driver of research and clinical practice. Data collection, storage, access, sharing, and analytics are dependent on the proper and consistent use of data management principles among the investigators. Since 2016, the FAIR (findable, accessible, interoperable, and reusable) guiding principles for research data management have been resonating in scientific communities. Enabling data to be findable, accessible, interoperable, and reusable is currently believed to strengthen data sharing,  reduce duplicated efforts, and move toward harmonization of data from heterogeneous unconnected data silos. FAIR initiatives and implementation trends are rising in different facets of scientific domains. It is important to understand the concepts and implementation practices of the FAIR data principles as applied to human health data by studying the flourishing initiatives and implementation lessons relevant to improved health research, particularly for data sharing during the coronavirus pandemic. Objective: This paper aims to conduct a scoping review to identify concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in the health data domain. Methods: The Arksey and O'Malley stage-based methodological framework for scoping reviews will be used for this review. PubMed, Web of Science, and Google Scholar will be searched to access relevant primary and grey publications. Articles written in English and published from 2014 onwards with FAIR principle concepts or practices in the health domain will be included. Duplication among the 3 data sources will be removed using a reference management software. The articles will then be exported to a systematic review management software. At least two independent authors will review the eligibility of each article based on defined inclusion and exclusion criteria. A pretested charting tool will be used to extract relevant information from the full-text papers. Qualitative thematic synthesis analysis methods will be employed by coding and developing themes. Themes will be derived from the research questions and contents in the included papers. Results: The results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews) reporting guidelines. We anticipate finalizing the manuscript for this work in 2021. Conclusions: We believe comprehensive information about the FAIR data principles, initiatives, implementation practices, and lessons learned in the FAIRification process in the health domain is paramount to supporting both evidence-based clinical practice and research transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID): PRR1-10.2196/22505 ",
doi="10.2196/22505",
url="https://www.researchprotocols.org/2021/2/e22505",
url="http://www.ncbi.nlm.nih.gov/pubmed/33528373"
}


@Article{info:doi/10.2196/14088,
author="Lee, Yun Hee
and Jin, Won Seok
and Henning-Smith, Carrie
and Lee, Jongwook
and Lee, Jaegoo",
title="Role of Health Literacy in Health-Related Information-Seeking Behavior Online: Cross-sectional Study",
journal="J Med Internet Res",
year="2021",
month="Jan",
day="27",
volume="23",
number="1",
pages="e14088",
keywords="digital divide",
keywords="health literacy",
keywords="internet",
keywords="technology",
keywords="access",
abstract="Background: The internet has emerged as a main venue of health information delivery and health-related activities. However, few studies have examined how health literacy determines online health-related behavior. Objective: The aim of this study was to investigate the current level of health-related information-seeking using the internet and how health literacy, access to technology, and sociodemographic characteristics impact health-related information-seeking behavior. Methods: We conducted a cross-sectional study through a survey with Minnesotan adults (N=614) to examine their health literacy, access to technology, and health-related information-seeking internet use. We used multivariate regression analysis to assess the relationship between health-related information-seeking on the internet and health literacy and access to technology, controlling for sociodemographic characteristics. Results: Better health literacy ($\beta$=.35, SE 0.12) and greater access to technological devices (eg, mobile phone and computer or tablet PC; $\beta$=.06, SE 0.19) were both associated with more health-related information-seeking behavior on the internet after adjusting for all other sociodemographic characteristics. Possession of a graduate degree ($\beta$=.28, SE 0.07), female gender ($\beta$=.15, SE 0.05), poor health ($\beta$=.22, SE 0.06), participation in social groups ($\beta$=.13, SE 0.05), and having an annual health exam ($\beta$=.35, SE 0.12) were all associated with online health-related information-seeking. Conclusions: Our findings indicate that access to online health-related information is not uniformly distributed throughout the population, which may exacerbate disparities in health and health care. Research, policy, and practice attention are needed to address the disparities in access to health information as well as to ensure the quality of the information and improve health literacy. ",
doi="10.2196/14088",
url="http://www.jmir.org/2021/1/e14088/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33502332"
}


@Article{info:doi/10.2196/14794,
author="Suzuki, Ryusuke
and Suzuki, Teppei
and Tsuji, Shintaro
and Fujiwara, Kensuke
and Yamashina, Hiroko
and Endoh, Akira
and Ogasawara, Katsuhiko",
title="A Bayesian Network--Based Browsing Model for Patients Seeking Radiology-Related Information on Hospital Websites: Development and Usability Study",
journal="J Med Internet Res",
year="2021",
month="Jan",
day="19",
volume="23",
number="1",
pages="e14794",
keywords="web marketing",
keywords="internet",
keywords="hospitals",
keywords="radiology",
keywords="information-seeking behavior",
abstract="Background: An increasing number of people are visiting hospital websites to seek better services and treatments compared to the past. It is therefore important for hospitals to develop websites to meet the needs of their patients. However, few studies have investigated whether and how the current hospital websites meet the patient's needs. Above all, in radiation departments, it may be difficult for patients to obtain the desired information regarding modality and diagnosis because such information is subdivided when described on a website. Objective: The purpose of this study is to suggest a hospital website search behavior model by analyzing the browsing behavior model using a Bayesian network from the perspective of one-to-one marketing. Methods: First, we followed the website access log of Hokkaido University Hospital, which was collected from September 1, 2016, to August 31, 2017, and analyzed the access log using Google Analytics. Second, we specified the access records related to radiology from visitor browsing pages and keywords. Third, using these resources, we structured 3 Bayesian network models based on specific patient needs: radiotherapy, nuclear medicine examination, and radiological diagnosis. Analyzing each model, this study considered why some visitors could not reach their desired page and improvements to meet the needs of visitors seeking radiology-related information. Results: The radiotherapy model showed that 74\% (67/90) of the target visitors could reach their requested page, but only 2\% (2/90) could reach the Center page where inspection information, one of their requested pages, is posted. By analyzing the behavior of the visitors, we clarified that connecting with the radiotherapy and radiological diagnosis pages is useful for increasing the proportion of patients reaching their requested page. Conclusions: We proposed solutions for patient web-browsing accessibility based on a Bayesian network. Further analysis is necessary to verify the accuracy of the proposed model in comparison to other models. It is expected that information provided on hospital websites will be improved using this method. ",
doi="10.2196/14794",
url="https://www.jmir.org/2021/1/e14794",
url="http://www.ncbi.nlm.nih.gov/pubmed/33464211"
}


@Article{info:doi/10.2196/24733,
author="Zhang, Di
and Hu, Hongchao
and Shi, Zhen
and Li, Biao",
title="Perceived Needs Versus Predisposing/Enabling Characteristics in Relation to Internet Cancer Information Seeking Among the US and Chinese Public: Comparative Survey Research",
journal="J Med Internet Res",
year="2021",
month="Jan",
day="11",
volume="23",
number="1",
pages="e24733",
keywords="HINTS",
keywords="health information seeking behavior (HISB)",
keywords="China",
keywords="United States",
keywords="comparative research",
keywords="cultural sensitivity",
abstract="Background: Since the rise of the internet, online health information seeking has become a worldwide phenomenon. However, health and health communication are inherently culture bound. A data-driven cross-country comparison enables us to better understand how cultural factors moderate the association between individual-level determinants and online health information seeking. Objective: The objective of the study was to examine similarities and differences in determinants of internet cancer information seeking between the US and Chinese general public (excluding cancer patients and survivors) under the framework of a behavioral model of health services use. Methods: This study used Health Information National Trends Survey (HINTS) 2017 (US data) and HINTS-China 2017 data to answer the research question. It focused on people with no cancer history and with internet access. For the HINTS 2017, the sample size was 2153; for the HINTS-China 2017, the sample size was 2358. To compare China and the United States, the researchers selected the same set of study variables for each dataset. Under the framework of the behavioral model of health services use, these predictors were predisposing factors, enabling factors, and need factors. Results: In terms of the predisposing factors, a higher age, college degree or above, being currently unemployed, and having a family history of cancer were associated with internet cancer information seeking for the Chinese respondents; none of these factors were related to information seeking for the US respondents, although a lower age was associated with information seeking. Regarding the enabling conditions, lower trust in family members and friends as reliable information sources was the only factor associated with information seeking for the Chinese respondents, while no enabling factor was related to information seeking for the US respondents. Regarding the need factors, perceived health status was not related to information seeking for the Chinese respondents, while perception of poorer health condition was related to information seeking for the US respondents. Higher cancer fear was related to information seeking for both groups, but the magnitude of association was smaller for the Chinese respondents than for the US respondents. Conclusions: Overall, under the framework of the behavioral model of health services use, the results based on multivariate logistic regression reveal clear patterns of cross-country/cultural differences in the factors associated with internet cancer information seeking behaviors: predisposing characteristics and enabling conditions are more important in China, while perceived needs are more significant in the US. Such differences might reflect possible US-China differences in job environment (eg, job pressure) and culture (individualism vs collectivism and family structure). ",
doi="10.2196/24733",
url="http://www.jmir.org/2021/1/e24733/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33427668"
}


@Article{info:doi/10.2196/18816,
author="van der Keylen, Piet
and Tomandl, Johanna
and Wollmann, Katharina
and M{\"o}hler, Ralph
and Sofroniou, Mario
and Maun, Andy
and Voigt-Radloff, Sebastian
and Frank, Luca",
title="The Online Health Information Needs of Family Physicians: Systematic Review of Qualitative and Quantitative Studies",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="30",
volume="22",
number="12",
pages="e18816",
keywords="family physicians",
keywords="general practitioners",
keywords="primary care",
keywords="needs",
keywords="barriers",
keywords="online information",
keywords="health information",
keywords="health resources",
keywords="internet",
keywords="information-seeking behaviors",
keywords="mobile phone",
abstract="Background: Digitalization and the increasing availability of online information have changed the way in which information is searched for and retrieved by the public and by health professionals. The technical developments in the last two decades have transformed the methods of information retrieval. Although systematic evidence exists on the general information needs of specialists, and in particular, family physicians (FPs), there have been no recent systematic reviews to specifically address the needs of FPs and any barriers that may exist to accessing online health information. Objective: This review aims to provide an up-to-date perspective on the needs of FPs in searching, retrieving, and using online information. Methods: This systematic review of qualitative and quantitative studies searched a multitude of databases spanning the years 2000 to 2020 (search date January 2020). Studies that analyzed the online information needs of FPs, any barriers to the accessibility of information, and their information-seeking behaviors were included. Two researchers independently scrutinized titles and abstracts, analyzing full-text papers for their eligibility, the studies therein, and the data obtained from them. Results: The initial search yielded 4541 studies for initial title and abstract screening. Of the 144 studies that were found to be eligible for full-text screening, 41 were finally included. A total of 20 themes were developed and summarized into 5 main categories: individual needs of FPs before the search; access needs, including factors that would facilitate or hinder information retrieval; quality needs of the information to hand; utilization needs of the information available; and implication needs for everyday practice. Conclusions: This review suggests that searching, accessing, and using online information, as well as any pre-existing needs, barriers, or demands, should not be perceived as separate entities but rather be regarded as a sequential process. Apart from accessing information and evaluating its quality, FPs expressed concerns regarding the applicability of this information to their everyday practice and its subsequent relevance to patient care. Future online information resources should cater to the needs of the primary care setting and seek to address the way in which such resources may be adapted to these specific requirements. ",
doi="10.2196/18816",
url="http://www.jmir.org/2020/12/e18816/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33377874"
}


@Article{info:doi/10.2196/19747,
author="Doherty, Cailbhe
and Joorabchi, Arash
and Megyesi, Peter
and Flynn, Aileen
and Caulfield, Brian",
title="Physiotherapists' Use of Web-Based Information Resources to Fulfill Their Information Needs During a Theoretical Examination: Randomized Crossover Trial",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="17",
volume="22",
number="12",
pages="e19747",
keywords="evidence-based medicine",
keywords="knowledge discovery",
keywords="information seeking behavior",
keywords="information dissemination",
keywords="information literacy",
keywords="online systems",
keywords="point-of-care systems",
keywords="mobile phone",
abstract="Background: The widespread availability of internet-connected smart devices in the health care setting has the potential to improve the delivery of research evidence to the care pathway and fulfill health care professionals' information needs. Objective: This study aims to evaluate the frequency with which physiotherapists experience information needs, the capacity of digital information resources to fulfill these needs, and the specific types of resources they use to do so. Methods: A total of 38 participants (all practicing physiotherapists; 19 females, 19 males) were randomly assigned to complete three 20-question multiple-choice questionnaire (MCQ) examinations under 3 conditions in a randomized crossover study design: assisted by a web browser, assisted by a federated search portal system, and unassisted. MCQ scores, times, and frequencies of information needs were recorded for overall examination-level and individual question-level analyses. Generalized estimating equations were used to assess differences between conditions for the primary outcomes. A log file analysis was conducted to evaluate participants' web search and retrieval behaviors. Results: Participants experienced an information need in 55.59\% (845/1520) MCQs (assisted conditions only) and exhibited a mean improvement of 10\% and 16\% in overall examination scores for the federated search and web browser conditions, respectively, compared with the unassisted condition (P<.001). In the web browser condition, Google was the most popular resource and the only search engine used, accounting for 1273 (64\%) of hits, followed by PubMed (195 hits; 10\% of total). In the federated search condition, Wikipedia and PubMed were the most popular resources with 1518 (46\% of total) and 1273 (39\% of total) hits, respectively. Conclusions: In agreement with the findings of previous research studies among medical physicians, the results of this study demonstrate that physiotherapists frequently experience information needs. This study provides new insights into the preferred digital information resources used by physiotherapists to fulfill these needs. Future research should clarify the implications of physiotherapists' apparent high reliance on Google, whether these results reflect the authentic clinical environment, and whether fulfilling clinical information needs alters practice behaviors or improves patient outcomes. ",
doi="10.2196/19747",
url="http://www.jmir.org/2020/12/e19747/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33331826"
}


@Article{info:doi/10.2196/23449,
author="Lazarus, V. Jeffrey
and Palayew, Adam
and Rasmussen, Neimann Lauge
and Andersen, Helms Tue
and Nicholson, Joey
and Norgaard, Ole",
title="Searching PubMed to Retrieve Publications on the COVID-19 Pandemic: Comparative Analysis of Search Strings",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="26",
volume="22",
number="11",
pages="e23449",
keywords="coronavirus",
keywords="COVID-19",
keywords="pandemic",
keywords="scientific publishing",
keywords="PubMed",
keywords="literature searching",
keywords="research",
keywords="literature",
keywords="search",
keywords="performance",
abstract="Background: Since it was declared a pandemic on March 11, 2020, COVID-19 has dominated headlines around the world and researchers have generated thousands of scientific articles about the disease. The fast speed of publication has challenged researchers and other stakeholders to keep up with the volume of published articles. To search the literature effectively, researchers use databases such as PubMed. Objective: The aim of this study is to evaluate the performance of different searches for COVID-19 records in PubMed and to assess the complexity of searches required. Methods: We tested PubMed searches for COVID-19 to identify which search string performed best according to standard metrics (sensitivity, precision, and F-score). We evaluated the performance of 8 different searches in PubMed during the first 10 weeks of the COVID-19 pandemic to investigate how complex a search string is needed. We also tested omitting hyphens and space characters as well as applying quotation marks. Results: The two most comprehensive search strings combining several free-text and indexed search terms performed best in terms of sensitivity (98.4\%/98.7\%) and F-score (96.5\%/95.7\%), but the single-term search COVID-19 performed best in terms of precision (95.3\%) and well in terms of sensitivity (94.4\%) and F-score (94.8\%). The term Wuhan virus performed the worst: 7.7\% for sensitivity, 78.1\% for precision, and 14.0\% for F-score. We found that deleting a hyphen or space character could omit a substantial number of records, especially when searching with SARS-CoV-2 as a single term. Conclusions: Comprehensive search strings combining free-text and indexed search terms performed better than single-term searches in PubMed, but not by a large margin compared to the single term COVID-19. For everyday searches, certain single-term searches that are entered correctly are probably sufficient, whereas more comprehensive searches should be used for systematic reviews. Still, we suggest additional measures that the US National Library of Medicine could take to support all PubMed users in searching the COVID-19 literature. ",
doi="10.2196/23449",
url="http://www.jmir.org/2020/11/e23449/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33197230"
}


@Article{info:doi/10.2196/medinform.6924,
author="Capurro, Daniel
and Barbe, Mario
and Daza, Claudio
and Santa Maria, Josefa
and Trincado, Javier",
title="Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis",
journal="JMIR Med Inform",
year="2020",
month="Nov",
day="24",
volume="8",
number="11",
pages="e6924",
keywords="digital phenotyping",
keywords="clinical data",
keywords="temporal abstraction",
abstract="Background: Inclusion criteria for observational studies frequently contain temporal entities and relations. The use of digital phenotypes to create cohorts in electronic health record--based observational studies requires rich functionality to capture these temporal entities and relations. However, such functionality is not usually available or requires complex database queries and specialized expertise to build them. Objective: The purpose of this study is to systematically assess observational studies reported in critical care literature to capture design requirements and functionalities for a graphical temporal abstraction-based digital phenotyping tool. Methods: We iteratively extracted attributes describing patients, interventions, and clinical outcomes. We qualitatively synthesized studies, identifying all temporal and nontemporal entities and relations. Results: We extracted data from 28 primary studies and 367 temporal and nontemporal entities. We generated a synthesis of entities, relations, and design patterns. Conclusions: We report on the observed types of clinical temporal entities and their relations as well as design requirements for a temporal abstraction-based digital phenotyping system. The results can be used to inform the development of such a system. ",
doi="10.2196/medinform.6924",
url="http://medinform.jmir.org/2020/11/e6924/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33231554"
}


@Article{info:doi/10.2196/17964,
author="Cox, Steven
and Ahalt, C. Stanley
and Balhoff, James
and Bizon, Chris
and Fecho, Karamarie
and Kebede, Yaphet
and Morton, Kenneth
and Tropsha, Alexander
and Wang, Patrick
and Xu, Hao",
title="Visualization Environment for Federated Knowledge Graphs: Development of an Interactive Biomedical Query Language and Web Application Interface",
journal="JMIR Med Inform",
year="2020",
month="Nov",
day="23",
volume="8",
number="11",
pages="e17964",
keywords="knowledge graphs",
keywords="clinical data",
keywords="biomedical data",
keywords="federation",
keywords="ontologies",
keywords="semantic harmonization",
keywords="visualization",
keywords="application programming interface",
keywords="translational science",
keywords="clinical practice",
abstract="Background: Efforts are underway to semantically integrate large biomedical knowledge graphs using common upper-level ontologies to federate graph-oriented application programming interfaces (APIs) to the data. However, federation poses several challenges, including query routing to appropriate knowledge sources, generation and evaluation of answer subsets, semantic merger of those answer subsets, and visualization and exploration of results. Objective: We aimed to develop an interactive environment for query, visualization, and deep exploration of federated knowledge graphs. Methods: We developed a biomedical query language and web application interphase---termed as Translator Query Language (TranQL)---to query semantically federated knowledge graphs and explore query results. TranQL uses the Biolink data model as an upper-level biomedical ontology and an API standard that has been adopted by the Biomedical Data Translator Consortium to specify a protocol for expressing a query as a graph of Biolink data elements compiled from statements in the TranQL query language. Queries are mapped to federated knowledge sources, and answers are merged into a knowledge graph, with mappings between the knowledge graph and specific elements of the query. The TranQL interactive web application includes a user interface to support user exploration of the federated knowledge graph. Results: We developed 2 real-world use cases to validate TranQL and address biomedical questions of relevance to translational science. The use cases posed questions that traversed 2 federated Translator API endpoints: Integrated Clinical and Environmental Exposures Service (ICEES) and Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP). ICEES provides open access to observational clinical and environmental data, and ROBOKOP provides access to linked biomedical entities, such as ``gene,'' ``chemical substance,'' and ``disease,'' that are derived largely from curated public data sources. We successfully posed queries to TranQL that traversed these endpoints and retrieved answers that we visualized and evaluated. Conclusions: TranQL can be used to ask questions of relevance to translational science, rapidly obtain answers that require assertions from a federation of knowledge sources, and provide valuable insights for translational research and clinical practice. ",
doi="10.2196/17964",
url="http://medinform.jmir.org/2020/11/e17964/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33226347"
}


@Article{info:doi/10.2196/14783,
author="Pajor, Margit Em{\'i}lia
and Eggers, Matthijs Sander
and de Vries, Hein
and Oenema, Anke",
title="Effects of Interactivity on Recall of Health Information: Experimental Study",
journal="J Med Internet Res",
year="2020",
month="Oct",
day="28",
volume="22",
number="10",
pages="e14783",
keywords="Interactivity",
keywords="cognitive involvement",
keywords="active control",
keywords="cognitive load",
keywords="recall",
keywords="need for cognition",
keywords="health literacy",
keywords="online health information",
keywords="information processing",
keywords="dietary supplements",
abstract="Background: Information provided in an interactive way is believed to be engaging because users can actively explore the information. Yet empirical findings often contradict this assumption. Consequently, there is still little known about whether and how interactivity affects communication outcomes such as recall. Objective: The aim of this study was to investigate mechanisms through which interactivity affects recall of online health information. We tested whether and how cognitive involvement, perceived active control, and cognitive load mediate the effects of interactivity on recall. In addition, we examined need for cognition and health literacy as potential moderators of the mediation effects. Given the increasing popularity of dietary supplement use, our health website focused on this topic. Methods: In an online between-subjects experiment (n=983), participants were randomly assigned to control condition (no interactive features), moderate interactivity (dropdown menus), and high interactivity (dropdown menus and responsive infographics). Two weeks before the experiment, background characteristics and moderating variables were measured. During website visit, data on users' online behavior were collected. Recall was measured postexposure. Results: Participants recalled significantly less information in the moderate (mean 3.48 [SD 2.71]) and high (mean 3.52 [SD 2.64]) interactivity conditions compared with the control condition (mean 5.63 [SD 2.18]). In the mediation analysis, we found direct, negative effects of moderate (b=--2.25, 95\% CI --2.59 to --1.90) and high (b=--2.16, 95\% CI --2.51 to --1.81) levels of interactivity on recall as well. In the relationship between interactivity and recall, cognitive involvement had a partial negative mediation effect (moderate interactivity: b=--.20; 95\% CI --0.31 to --0.10; high interactivity: b=--.21, 95\% CI --0.33 to --0.10) and perceived active control had a partial positive mediation effect (moderate interactivity: b=.28, 95\% CI 0.18 to 0.40; high interactivity: b=.27, 95\% CI 0.16 to 0.40). Conclusions: Interactivity decreased recall. In addition, through interactivity participants were less involved with the content of the information, yet they felt they had more control over the information. These effects were stronger in the high need for cognition and high health literate groups compared with their counterparts. ",
doi="10.2196/14783",
url="https://www.jmir.org/2020/10/e14783",
url="http://www.ncbi.nlm.nih.gov/pubmed/33112245"
}


@Article{info:doi/10.2196/18581,
author="Scheerer, Cora
and R{\"u}th, Melvin
and Tizek, Linda
and K{\"o}berle, Martin
and Biedermann, Tilo
and Zink, Alexander",
title="Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018",
journal="J Med Internet Res",
year="2020",
month="Oct",
day="16",
volume="22",
number="10",
pages="e18581",
keywords="Google",
keywords="infodemiology",
keywords="infoveillance",
keywords="public health",
keywords="seasonal health trend",
keywords="medical internet research",
keywords="tick-borne disease",
keywords="tick bites, borreliosis",
keywords="Lyme disease",
abstract="Background: Borreliosis is the most frequently transmitted tick-borne disease in Europe. It is difficult to estimate the incidence of tick bites and associated diseases in the German population due to the lack of an obligation to register across all 16 federal states of Germany. Objective: The aim of this study is to show that Google data can be used to generate general trends of infectious diseases on the basis of borreliosis and tick bites. In addition, the possibility of using Google AdWord data to estimate incidences of infectious diseases, where there is inconsistency in the obligation to notify authorities, is investigated with the perspective to facilitate public health studies. Methods: Google AdWords Keyword Planner was used to identify search terms related to ticks and borreliosis in Germany from January 2015 to December 2018. The search volume data from the identified search terms was assessed using Excel version 15.23. In addition, SPSS version 24.0 was used to calculate the correlation between search volumes, registered cases, and temperature. Results: A total of 1999 tick-related and 542 borreliosis-related search terms were identified, with a total of 209,679,640 Google searches in all 16 German federal states in the period under review. The analysis showed a high correlation between temperature and borreliosis (r=0.88), and temperature and tick bite (r=0.83), and a very high correlation between borreliosis and tick bite (r=0.94). Furthermore, a high to very high correlation between Google searches and registered cases in each federal state was observed (Brandenburg r=0.80, Mecklenburg-West Pomerania r= 0.77, Saxony r= 0.74, and Saxony-Anhalt r=0.90; all P<.001). Conclusions: Our study provides insight into annual trends concerning interest in ticks and borreliosis that are relevant to the German population exemplary in the data of a large internet search engine. Public health studies collecting incidence data may benefit from the results indicating a significant correlation between internet search data and incidences of infectious diseases. ",
doi="10.2196/18581",
url="http://www.jmir.org/2020/10/e18581/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33064086"
}


@Article{info:doi/10.2196/19985,
author="Kubb, Christian
and Foran, M. Heather",
title="Online Health Information Seeking by Parents for Their Children: Systematic Review and Agenda for Further Research",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="25",
volume="22",
number="8",
pages="e19985",
keywords="information seeking behavior",
keywords="parents",
keywords="child",
keywords="internet",
keywords="health behavior",
keywords="digital health",
abstract="Background: Parents commonly use the internet to search for information about their child's health-related symptoms and guide parental health-related decisions. Despite the impact of parental online health seeking on offline health behaviors, this area of research remains understudied. Previous literature has not adequately distinguished searched behaviors when searching for oneself or one`s child. Objective: The purpose of this review is to examine prevalences and associated variables of parent-child online health information seeking; investigate parents' health-related online behavior regarding how they find, use, and evaluate information; and identify barriers and concerns that they experience during the search. Based on this analysis, we develop a conceptual model of potentially important variables of proxy online health information seeking, with a focus on building an agenda for further research. Methods: We conducted a comprehensive systematic literature review of the PsycINFO, JMIR, and PubMed electronic databases. Studies between January 1994 and June 2018 were considered. The conceptual model was developed using an inductive mixed methods approach based on the investigated variables in the study sample. Results: A total of 33 studies met the inclusion criteria. Findings suggest that parents worldwide are heavy online users of health-related information for their children across highly diverse circumstances. A total of 6 studies found high parental health anxiety, with prevalences ranging from 14\% to 52\%. Although parents reported wishing for more guidance from their pediatrician on how to find reliable information, they rarely discussed retrieved information from the web. The conceptual model of proxy online health information seeking includes 49 variables. Conclusions: This systematic review identifies important gaps regarding the influence of health-related information on parents' health behavior and outcomes. Follow-up studies are required to offer parents guidance on how to use the web for health purposes in an effective way, as well as solutions to the multifaceted problems during or after online health information seeking for their child. The conceptual model with the number of studies in each model category listed highlights how previous studies have hardly considered relational variables between the parent and child. An agenda for future research is presented. ",
doi="10.2196/19985",
url="http://www.jmir.org/2020/8/e19985/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32840484"
}


@Article{info:doi/10.2196/16138,
author="Xie, Juan
and Xie, Shi
and Cheng, Ying
and He, Zhe",
title="Beliefs and Information Seeking in Patients With Cancer in Southwest China: Survey Study",
journal="JMIR Cancer",
year="2020",
month="Aug",
day="21",
volume="6",
number="2",
pages="e16138",
keywords="cancer information seeking",
keywords="cancer belief",
keywords="fatalism",
keywords="southwest China",
abstract="Background: Although previous studies have reported the cancer information-seeking behaviors among patients in high-income countries, the cancer information-seeking practices of patients living in low- and middle-income areas are less known. Objective: This study investigated the beliefs and information-seeking patterns of cancer patients in southwest China. Methods: A questionnaire was designed, and data were collected in two hospitals (N=285) in southwest China. Statistical analyses included bivariate analyses and regressions. Results: Patients' attitudes towards cancer fatalism were significantly influenced by marital status (P<.001), education (P<.001), and household income (P<.001). Moreover, endorsing fatalistic belief was positively associated with age (r=0.35, P<.001). The regression model showed that younger patients (odds ratio [OR] 0.96, 95\% CI 0.93-0.99) and those with higher education (OR 1.75, 95\% CI 1.09-2.81) were more likely to seek information. Additionally, patients who were less confident in getting information were more likely to find information (OR 1.70, 95\% CI 1.15-2.52), while fatalism belief was not significant in the regression (OR 0.65, 95\% CI 0.22-1.95). Conclusions: This study explored the information-seeking patterns of cancer patients in southwest China. It was found that many Chinese people endorsed cancer fatalism. These pessimistic beliefs about the potential to prevent and to cure cancer correlate with rather than cause cancer-related information seeking. However, self-efficacy about the confidence in finding needed cancer information was a significant predictor of information-seeking. ",
doi="10.2196/16138",
url="http://cancer.jmir.org/2020/2/e16138/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32821061"
}


@Article{info:doi/10.2196/16422,
author="Bao, Hongchang
and Baker, O. Christopher J.
and Adisesh, Anil",
title="Occupation Coding of Job Titles: Iterative Development of an Automated Coding Algorithm for the Canadian National Occupation Classification (ACA-NOC)",
journal="JMIR Form Res",
year="2020",
month="Aug",
day="5",
volume="4",
number="8",
pages="e16422",
keywords="occupation coding",
keywords="automated coding",
keywords="occupational health",
keywords="job title",
abstract="Background: In many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. Such information is usually solicited during interviews with open-ended questions such as ``What is your job?'' and ``What industry sector do you work in?'' Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation. Objective: This study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm. Methods: We developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes. Results: The ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7\% (332/566) and improved when broader job categories were considered (65.0\% at the three-digit NOC code level, 72.3\% at the two-digit NOC code level, and 81.6\% at the one-digit NOC code level). Conclusions: The ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets. ",
doi="10.2196/16422",
url="https://formative.jmir.org/2020/8/e16422",
url="http://www.ncbi.nlm.nih.gov/pubmed/32755893"
}


@Article{info:doi/10.2196/17853,
author="Zowalla, Richard
and Wetter, Thomas
and Pfeifer, Daniel",
title="Crawling the German Health Web: Exploratory Study and Graph Analysis",
journal="J Med Internet Res",
year="2020",
month="Jul",
day="24",
volume="22",
number="7",
pages="e17853",
keywords="health information",
keywords="internet",
keywords="web crawling",
keywords="distributed system",
abstract="Background: The internet has become an increasingly important resource for health information. However, with a growing amount of web pages, it is nearly impossible for humans to manually keep track of evolving and continuously changing content in the health domain. To better understand the nature of all web-based health information as given in a specific language, it is important to identify (1) information hubs for the health domain, (2) content providers of high prestige, and (3) important topics and trends in the health-related web. In this context, an automatic web crawling approach can provide the necessary data for a computational and statistical analysis to answer (1) to (3). Objective: This study demonstrates the suitability of a focused crawler for the acquisition of the German Health Web (GHW) which includes all health-related web content of the three mostly German speaking countries Germany, Austria and Switzerland. Based on the gathered data, we provide a preliminary analysis of the GHW's graph structure covering its size, most important content providers and a ratio of public to private stakeholders. In addition, we provide our experiences in building and operating such a highly scalable crawler. Methods: A support vector machine classifier was trained on a large data set acquired from various German content providers to distinguish between health-related and non--health-related web pages. The classifier was evaluated using accuracy, recall and precision on an 80/20 training/test split (TD1) and against a crowd-validated data set (TD2). To implement the crawler, we extended the open-source framework StormCrawler. The actual crawl was conducted for 227 days. The crawler was evaluated by using harvest rate and its recall was estimated using a seed-target approach. Results: In total, n=22,405 seed URLs with country-code top level domains .de: 85.36\% (19,126/22,405), .at: 6.83\% (1530/22,405), .ch: 7.81\% (1749/22,405), were collected from Curlie and a previous crawl. The text classifier achieved an accuracy on TD1 of 0.937 (TD2=0.966), a precision on TD1 of 0.934 (TD2=0.954) and a recall on TD1 of 0.944 (TD2=0.989). The crawl yields 13.5 million presumably relevant and 119.5 million nonrelevant web pages. The average harvest rate was 19.76\%; recall was 0.821 (4105/5000 targets found). The resulting host-aggregated graph contains 215,372 nodes and 403,175 edges (network diameter=25; average path length=6.466; average degree=1.872; average in-degree=1.892; average out-degree=1.845; modularity=0.723). Among the 25 top-ranked pages for each country (according to PageRank), 40\% (30/75) were web sites published by public institutions. 25\% (19/75) were published by nonprofit organizations and 35\% (26/75) by private organizations or individuals. Conclusions: The results indicate, that the presented crawler is a suitable method for acquiring a large fraction of the GHW. As desired, the computed statistical data allows for determining major information hubs and important content providers on the GHW. In the future, the acquired data may be used to assess important topics and trends but also to build health-specific search engines. ",
doi="10.2196/17853",
url="http://www.jmir.org/2020/7/e17853/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32706701"
}


@Article{info:doi/10.2196/17502,
author="Moreno-Fern{\'a}ndez, Manuela Mar{\'i}a
and Matute, Helena",
title="Biased Sampling and Causal Estimation of Health-Related Information: Laboratory-Based Experimental Research",
journal="J Med Internet Res",
year="2020",
month="Jul",
day="24",
volume="22",
number="7",
pages="e17502",
keywords="information sampling",
keywords="causal illusion",
keywords="causal bias",
keywords="health information",
keywords="health communication",
abstract="Background: The internet is a relevant source of health-related information. The huge amount of information available on the internet forces users to engage in an active process of information selection. Previous research conducted in the field of experimental psychology showed that information selection itself may promote the development of erroneous beliefs, even if the information collected does not. Objective: The aim of this study was to assess the relationship between information searching strategy (ie, which cues are used to guide information retrieval) and causal inferences about health while controlling for the effect of additional information features. Methods: We adapted a standard laboratory task that has previously been used in research on contingency learning to mimic an information searching situation. Participants (N=193) were asked to gather information to determine whether a fictitious drug caused an allergic reaction. They collected individual pieces of evidence in order to support or reject the causal relationship between the two events by inspecting individual cases in which the drug was or was not used or in which the allergic reaction appeared or not. Thus, one group (cause group, n=105) was allowed to sample information based on the potential cause, whereas a second group (effect group, n=88) was allowed to sample information based on the effect. Although participants could select which medical records they wanted to check---cases in which the medicine was used or not (in the cause group) or cases in which the effect appeared or not (in the effect group)---they all received similar evidence that indicated the absence of a causal link between the drug and the reaction. After observing 40 cases, they estimated the drug--allergic reaction causal relationship. Results: Participants used different strategies for collecting information. In some cases, participants displayed a biased sampling strategy compatible with positive testing, that is, they required a high proportion of evidence in which the drug was administered (in the cause group) or in which the allergic reaction appeared (in the effect group). Biased strategies produced an overrepresentation of certain pieces of evidence at the detriment of the representation of others, which was associated with the accuracy of causal inferences. Thus, how the information was collected (sampling strategy) demonstrated a significant effect on causal inferences (F1,185=32.53, P<.001, $\eta$2p=0.15) suggesting that inferences of the causal relationship between events are related to how the information is gathered. Conclusions: Mistaken beliefs about health may arise from accurate pieces of information partially because of the way in which information is collected. Patient or person autonomy in gathering health information through the internet, for instance, may contribute to the development of false beliefs from accurate pieces of information because search strategies can be biased. ",
doi="10.2196/17502",
url="http://www.jmir.org/2020/7/e17502/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32706735"
}


@Article{info:doi/10.2196/12799,
author="Massonnaud, R. Cl{\'e}ment
and Kerdelhu{\'e}, Ga{\'e}tan
and Grosjean, Julien
and Lelong, Romain
and Griffon, Nicolas
and Darmoni, J. Stefan",
title="Identification of the Best Semantic Expansion to Query PubMed Through Automatic Performance Assessment of Four Search Strategies on All Medical Subject Heading Descriptors: Comparative Study",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="4",
volume="8",
number="6",
pages="e12799",
keywords="bibliographic database",
keywords="information retrieval",
keywords="literature search",
keywords="Medical Subject Headings",
keywords="MEDLINE",
keywords="PubMed",
keywords="precision",
keywords="recall",
keywords="search strategy",
keywords="thesaurus",
abstract="Background: With the continuous expansion of available biomedical data, efficient and effective information retrieval has become of utmost importance. Semantic expansion of queries using synonyms may improve information retrieval. Objective: The aim of this study was to automatically construct and evaluate expanded PubMed queries of the form ``preferred term''[MH] OR ``preferred term''[TIAB] OR ``synonym 1''[TIAB] OR ``synonym 2''[TIAB] OR \ldots, for each of the 28,313 Medical Subject Heading (MeSH) descriptors, by using different semantic expansion strategies. We sought to propose an innovative method that could automatically evaluate these strategies, based on the three main metrics used in information science (precision, recall, and F-measure). Methods: Three semantic expansion strategies were assessed. They differed by the synonyms used to build the queries as follows: MeSH synonyms, Unified Medical Language System (UMLS) mappings, and custom mappings (Catalogue et Index des Sites M{\'e}dicaux de langue Fran{\c{c}}aise [CISMeF]). The precision, recall, and F-measure metrics were automatically computed for the three strategies and for the standard automatic term mapping (ATM) of PubMed. The method to automatically compute the metrics involved computing the number of all relevant citations (A), using National Library of Medicine indexing as the gold standard (``preferred term''[MH]), the number of citations retrieved by the added terms (''synonym 1``[TIAB] OR ''synonym 2``[TIAB] OR \ldots) (B), and the number of relevant citations retrieved by the added terms (combining the previous two queries with an ``AND'' operator) (C). It was possible to programmatically compute the metrics for each strategy using each of the 28,313 MeSH descriptors as a ``preferred term,'' corresponding to 239,724 different queries built and sent to the PubMed application program interface. The four search strategies were ranked and compared for each metric. Results: ATM had the worst performance for all three metrics among the four strategies. The MeSH strategy had the best mean precision (51\%, SD 23\%). The UMLS strategy had the best recall and F-measure (41\%, SD 31\% and 36\%, SD 24\%, respectively). CISMeF had the second best recall and F-measure (40\%, SD 31\% and 35\%, SD 24\%, respectively). However, considering a cutoff of 5\%, CISMeF had better precision than UMLS for 1180 descriptors, better recall for 793 descriptors, and better F-measure for 678 descriptors. Conclusions: This study highlights the importance of using semantic expansion strategies to improve information retrieval. However, the performances of a given strategy, relatively to another, varied greatly depending on the MeSH descriptor. These results confirm there is no ideal search strategy for all descriptors. Different semantic expansions should be used depending on the descriptor and the user's objectives. Thus, we developed an interface that allows users to input a descriptor and then proposes the best semantic expansion to maximize the three main metrics (precision, recall, and F-measure). ",
doi="10.2196/12799",
url="https://medinform.jmir.org/2020/6/e12799",
url="http://www.ncbi.nlm.nih.gov/pubmed/32496201"
}


@Article{info:doi/10.2196/16281,
author="Maitz, Emanuel
and Maitz, Katharina
and Sendlhofer, Gerald
and Wolfsberger, Christina
and Mautner, Selma
and Kamolz, Lars-Peter
and Gasteiger-Klicpera, Barbara",
title="Internet-Based Health Information--Seeking Behavior of Students Aged 12 to 14 Years: Mixed Methods Study",
journal="J Med Internet Res",
year="2020",
month="May",
day="26",
volume="22",
number="5",
pages="e16281",
keywords="internet-based health information--seeking behavior",
keywords="eHealth literacy",
keywords="children and adolescents",
keywords="mixed methods study",
abstract="Background: Many children and adolescents are surrounded by smartphones, tablets, and computers and know how to search the internet for almost any information. However, very few of them know how to select proper information from reliable sources. This can become a problem when health issues are concerned, where it is vital to identify incorrect or misleading information. The competence to critically evaluate digital information on health issues is of increasing importance for adolescents. Objective: The aim of this study was to assess how children and adolescents rate their internet-based health literacy and how their actual literacy differs from their ratings. In addition, there was a question on how their search performance is related to their self-efficacy. To evaluate these questions, a criteria-based analysis of the quality of the websites they visited was performed. Finally, the possibility to increase their internet-based health literacy in a 3-day workshop was explored. Methods: A workshop with a focus on health literacy was attended by 14 children and adolescents in an Austrian secondary school. After prior assessments (Culture Fair Intelligence Test, revised German version; Reading Speed and Reading Comprehension Test for Grades 6 to 12, German; electronic health literacy scale [eHEALS]; and General Self-Efficacy Scale, Reversed Version, German), the students were asked to perform an internet-based search on a health-related issue. Browser histories and screenshots of all internet searches were gathered, clustered, and analyzed. After the workshop, the health literacy of the students was assessed again by using the eHEALS. Results: The 14 students opened a total of 85 homepages, but only eight of these homepages were rated as good or fair by two experts (independent rating) based on specific criteria. The analysis showed that the students judged their own internet-based health literacy much higher than the actual value, and students who had rated themselves better did not visit websites of high quality. Internet-based health literacy correlated significantly with the self-efficacy of the students (rs=0.794, P=.002). Conclusions: Our study showed that it is possible to draw the attention of students to critical aspects of internet search and to slightly improve their search competence in a workshop. Targeted improvement of health literacy is urgently required, and students need special instruction for this purpose. Further investigations in this area with larger sets of data, which could be feasible with the help of a computer program, are urgently needed. ",
doi="10.2196/16281",
url="https://www.jmir.org/2020/5/e16281",
url="http://www.ncbi.nlm.nih.gov/pubmed/32209532"
}


@Article{info:doi/10.2196/15817,
author="Niu, Zhaomeng
and Willoughby, Fitts Jessica
and Mei, Jing
and Li, Shaochun
and Hu, Pengwei",
title="A Cross-Cultural Comparison of an Extended Planned Risk Information Seeking Model on Mental Health Among College Students: Cross-Sectional Study",
journal="J Med Internet Res",
year="2020",
month="May",
day="11",
volume="22",
number="5",
pages="e15817",
keywords="information seeking behavior",
keywords="mental health",
keywords="cross-cultural comparison",
abstract="Background: Approximately 42.5 million adults have been affected by mental illness in the United States in 2013, and 173 million people have been affected by a diagnosable psychiatric disorder in China. An increasing number of people tend to seek health information on the Web, and it is important to understand the factors associated with individuals' mental health information seeking. Identifying factors associated with mental health information seeking may influence the disease progression of potential patients. The planned risk information seeking model (PRISM) was developed in 2010 by integrating multiple information seeking models including the theory of planned behavior. Few studies have replicated PRISM outside the United States and no previous study has examined mental health as a personal risk in different cultures.  Objective: This study aimed to test the planned risk information seeking model (PRISM) in China and the United States with a chronic disease, mental illness, and two additional factors, ie, media use and cultural identity, among college students. Methods: Data were collected in both countries using the same online survey through a survey management program (Qualtrics). In China, college instructors distributed the survey link among university students, and it was also posted on a leading social media site called Sina Weibo. In the United States, the data were collected in a college-wide survey pool in a large Northwestern university. Results: The final sample size was 235 for the Chinese sample and 241 for the US sample. Media use was significantly associated with mental health information--seeking intentions in the Chinese sample (P<.001), and cultural identity was significantly associated with intentions in both samples (China: P=.02; United States: P<.001). The extended PRISM had a better model fit than the original PRISM. Conclusions: Cultural identity and media use should be considered when evaluating the process of mental health information seeking or when designing interventions to address mental health information seeking. ",
doi="10.2196/15817",
url="http://www.jmir.org/2020/5/e15817/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32441654"
}


@Article{info:doi/10.2196/16777,
author="Seguin, Aur{\'e}lie
and Haynes, Brian Robert
and Carballo, Sebastian
and Iorio, Alfonso
and Perrier, Arnaud
and Agoritsas, Thomas",
title="Translating Clinical Questions by Physicians Into Searchable Queries: Analytical Survey Study",
journal="JMIR Med Educ",
year="2020",
month="Apr",
day="20",
volume="6",
number="1",
pages="e16777",
keywords="evidence-based medicine",
keywords="evidence retrieval",
keywords="Web-based resources",
keywords="search engines",
keywords="search taxonomy",
keywords="clinical information science",
abstract="Background: Staying up to date and answering clinical questions with current best evidence from health research is challenging. Evidence-based clinical texts, databases, and tools can help, but clinicians first need to translate their clinical questions into searchable queries. MacPLUS FS (McMaster Premium LiteratUre Service Federated Search) is an online search engine that allows clinicians to explore multiple resources simultaneously and retrieves one single output that includes the following: (1) evidence from summaries (eg, UpToDate and DynaMed), (2) preappraised research (eg, EvidenceAlerts), and (3) non-preappraised research (eg, PubMed), with and without validated bibliographic search filters. MacPLUS FS can also be used as a laboratory to explore clinical questions and evidence retrieval. Objective: Our primary objective was to examine how clinicians formulate their queries on a federated search engine, according to the population, intervention, comparison, and outcome (PICO) framework. Our secondary objective was to assess which resources were accessed by clinicians to answer their questions. Methods: We performed an analytical survey among 908 clinicians who used MacPLUS FS in the context of a randomized controlled trial on search retrieval. Recording account log-ins and usage, we extracted all 1085 queries performed during a 6-month period and classified each search term according to the PICO framework. We further categorized queries into background (eg, ``What is porphyria?'') and foreground questions (eg, ``Does treatment A work better than B?''). We then analyzed the type of resources that clinicians accessed. Results: There were 695 structured queries, after exclusion of meaningless queries and iterations of similar searches. We classified 56.5\% (393/695) of these queries as background questions and 43.5\% (302/695) as foreground questions, the majority of which were related to questions about therapy (213/695, 30.6\%), followed by diagnosis (48/695, 6.9\%), etiology (24/695, 3.5\%), and prognosis (17/695, 2.5\%). This distribution did not significantly differ between postgraduate residents and medical faculty physicians (P=.51). Queries included a median of 3 search terms (IQR 2-4), most often related to the population and intervention or test, rarely related to the outcome, and never related to the comparator. About half of the resources accessed (314/610, 51.5\%) were summaries, 24.4\% (149/610) were preappraised research, and 24.1\% were (147/610) non-preappraised research. Conclusions: Our results, from a large sample of real-life queries, could guide the development of educational interventions to improve clinicians' retrieval skills, as well as inform the design of more useful evidence-based resources for clinical practice. Trial Registration: ClinicalTrials.gov NCT02038439; https://www.clinicaltrials.gov/ct2/show/NCT02038439 ",
doi="10.2196/16777",
url="http://mededu.jmir.org/2020/1/e16777/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32310137"
}


@Article{info:doi/10.2196/15304,
author="Gaiser, Meike
and Kirsch, Joachim
and Mutzbauer, Sebastian Till",
title="Using Nonexpert Online Reports to Enhance Expert Knowledge About Causes of Death in Dental Offices Reported in Scientific Publications: Qualitative and Quantitative Content Analysis and Search Engine Analysis",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="17",
volume="22",
number="4",
pages="e15304",
keywords="dental death",
keywords="dental practice",
keywords="dental sedation",
keywords="risk",
keywords="internet search engine",
abstract="Background: Fatalities rarely occur in dental offices. Implications for clinicians may be deduced from scientific publications and internet reports about deaths in dental offices. Objective: Data involving deaths in dental facilities were analyzed using Google as well as the PubMed database. By comparing both sources, we examined how internet data may enhance knowledge about deaths in dental offices obtained from scientific medical publications, which causes of death are published online, and how associated life-threatening emergencies may be prevented. Methods: To retrieve relevant information, we searched Google for country-specific incidents of death in dental practices using the following keywords: ``death at the dentist,'' ``death in dental practice,'' and ``dying at the dentist.'' For PubMed searches, the following keywords were used: ``dentistry and mortality,'' ``death and dental treatment,'' ``dentistry and fatal outcome,'' and ``death and dentistry.'' Deaths associated with dental treatment in a dental facility, attributable causes of death, and documented ages of the deceased were included in our analysis. Deaths occurring in maxillofacial surgery or pre-existing diseases involved in the death (eg, cancer and abscesses) were excluded. A total of 128 cases from online publications and 71 cases from PubMed publications that met the inclusion criteria were analyzed using chi-square statistics after exclusion of duplicates. Results: The comparison between the fatalities from internet (n=117) and PubMed (n=71) publications revealed that more casualties affecting minors appeared online than in PubMed literature (online 68/117, 58.1\%; PubMed 20/71, 28\%; P<.001). In PubMed articles, 10 fatalities in patients older than 70 years of age were described, while online sources published 5 fatalities (P=.02). Most deaths, both from internet publications and PubMed literature, could be assigned to the category anesthesia, medication, or sedation (online 80/117, 68.4\%; PubMed 25/71, 35\%; P<.001). Deaths assigned to the categories infection and cardiovascular system appeared more often in the PubMed literature (infection: online 10/117, 8.5\%; PubMed 15/71, 21\%; P=.01; cardiovascular system: online 5/117, 4.3\%; PubMed 15/71, 21\%; P<.001). Furthermore, sedative drugs were involved in a larger proportion of fatal incidents listed online compared to in PubMed (online 41/117, 35.0\%; PubMed: 14/71, 20\%, P=.03). In the United States, more deaths occurred under sedation (44/96, 46\%) compared to those in the other countries (Germany and Austria 1/17, 6\%, P=.002; United Kingdom 1/14, 7\%, P=.006). Conclusions: Online and PubMed databases may increase awareness of life-threatening risks for patients during dental treatment. Negative aspects of anesthesia and sedation, as well as the number of deaths of young patients, were underestimated when reviewing PubMed literature only. Medical history of patients, medication dosages, and vital function monitoring are significant issues for practitioners. A high-impact finding from online reports was the underestimation of risks when performing sedation and even general anesthesia. Detailed knowledge of the definition and understanding of deep sedation and general anesthesia by dentists is of major concern. By avoiding potentially hazardous procedures, such as sedation-aided treatments performed solely by dentists, the risk of treatment-induced life-threatening emergencies may be reduced. ",
doi="10.2196/15304",
url="http://www.jmir.org/2020/4/e15304/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32038029"
}


@Article{info:doi/10.2196/13369,
author="Taira, Kazuya
and Murayama, Taichi
and Fujita, Sumio
and Ito, Mikiko
and Kamide, Kei
and Aramaki, Eiji",
title="Comparing Medical Term Usage Patterns of Professionals and Search Engine and Community Question Answering Service Users in Japan: Log Analysis",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="13",
volume="22",
number="4",
pages="e13369",
keywords="health knowledge",
keywords="internet",
keywords="search engine",
keywords="community question answering service",
keywords="information-seeking behavior",
abstract="Background: Despite increasing opportunities for acquiring health information online, discussion of the specific words used in searches has been limited. Objective: The aim of this study was to clarify the medical information gap between medical professionals and the general public in Japan through health information--seeking activities on the internet. Methods: Search and posting data were analyzed from one of the most popular domestic search engines in Japan (Yahoo! JAPAN Search) and the most popular Japanese community question answering service (Yahoo! Chiebukuro). We compared the frequency of 100 clinical words appearing in the clinical case reports of medical professionals (clinical frequency) with their frequency in Yahoo! JAPAN Search (search frequency) logs and questions posted to Yahoo! Chiebukuro (question frequency). The Spearman correlation coefficient was used to quantify association patterns among the three information sources. Additionally, user information (gender and age) in the search frequency associated with each registered user was extracted. Results: Significant correlations were observed between clinical and search frequencies (r=0.29, P=.003), clinical and question frequencies (r=0.34, P=.001), and  search and question frequencies (r=0.57, P<.001). Low-frequency words in clinical frequency (eg, ``hypothyroidism,'' ``ulcerative colitis'') highly ranked in search frequency. Similarly, ``pain,'' ``slight fever,'' and ``numbness'' were highly ranked only in question frequency. The weighted average of ages was 34.5 (SD 2.7) years, and the weighted average of gender (man --1, woman +1) was 0.1 (SD 0.1) in search frequency. Some words were specifically extracted from the search frequency of certain age groups, including ``abdominal pain'' (10-20 years), ``plasma cells'' and ``inflammatory findings'' (20-30 years), ``DM'' (diabetes mellitus; 30-40 years), ``abnormal shadow'' and ``inflammatory findings'' (40-50 years), ``hypertension'' and ``abnormal shadow'' (50-60 years), and ``lung cancer'' and ``gastric cancer'' (60-70 years). Conclusions: Search and question frequencies showed similar tendencies, whereas search and clinical frequencies showed discrepancy. Low-clinical frequency words related to diseases such as ``hypothyroidism'' and ``ulcerative colitis'' had high search frequencies, whereas those related to symptoms such as ``pain,'' ``slight fever,'' and ``numbness'' had high question frequencies. Moreover, high search frequency words included designated intractable diseases such as ``ulcerative colitis,'' which has an incidence of less than 0.1\% in the Japanese population. Therefore, it is generally worthwhile to pay attention not only to major diseases but also to minor diseases that users frequently seek information on, and more words will need to be analyzed in the future. Some characteristic words for certain age groups were observed (eg, 20-40 years: ``cancer''; 40-60 years: diagnoses and diseases identified in health examinations; 60-70 years: diseases with late adulthood onset and ``death''). Overall, this analysis demonstrates that medical professionals as information providers should be aware of clinical frequency, and medical information gaps between professionals and the general public should be bridged. ",
doi="10.2196/13369",
url="https://www.jmir.org/2020/4/e13369",
url="http://www.ncbi.nlm.nih.gov/pubmed/32281938"
}


@Article{info:doi/10.2196/15906,
author="Sedrak, S. Mina
and Soto-Perez-De-Celis, Enrique
and Nelson, A. Rebecca
and Liu, Jennifer
and Waring, E. Molly
and Lane, S. Dorothy
and Paskett, D. Electra
and Chlebowski, T. Rowan",
title="Online Health Information--Seeking Among Older Women With Chronic Illness: Analysis of the Women's Health Initiative",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="9",
volume="22",
number="4",
pages="e15906",
keywords="online health information--seeking",
keywords="digital health",
keywords="technology",
keywords="chronic disease",
keywords="internet",
abstract="Background: Understanding how older patients with chronic illnesses use the internet to obtain health information is relevant for the design of digital interventions aimed at improving the health and well-being of adults aged 65 years and older; this cohort represents the sickest, most expensive, and fastest-growing segment of the US population. Objective: The objective of our study was to describe online health information--seeking behavior among older patients with chronic illnesses and to compare the characteristics of patients who report using the internet to obtain health information with those who do not. Methods: The study population included 72,806 women aged 65 years and older enrolled in the Women's Health Initiative (WHI), a national cohort study, who completed a 2014 supplemental questionnaire assessing everyday technology use and internet use for researching health conditions. Comparisons were made between participants with and without a history of chronic illness and between users and nonusers of online sources for health information. Multivariate logistic regression was used to estimate odds ratios (ORs) and 95\% CIs. Results: Of the total, 59\% (42,887/72,806) of older women used the internet for health information. Compared with women who did not use the internet to obtain health information, those who used the internet were younger (median age: 76 vs 81 years), more likely to be non-Hispanic white (90\% [38,481/42,887] vs 87\% [26,017/29,919]), earned a higher income (over \$US 50,000: 55\% [23,410/42,887] vs 33\% [9991/29,919]), achieved a higher educational level (more than high school: 87\% [37,493/42,887] vs 75\% [22,377/29,919]), and were more likely to live with a partner (52\% [22,457/42,887] vs 36\% [10,759/29,919]) (all P<.001). Women with Alzheimer disease were least likely to report online health information--seeking compared to those without the disease (OR 0.41, 95\% CI 0.38-0.43). In contrast, women with a recent diagnosis of cancer, within the previous 2 years (OR 1.23, 95\% CI 1.11-1.36) or 2-5 years ago (OR 1.09, 95\% CI 1.00-1.19), were most likely to use the internet for health information. Conclusions: Nearly 6 in 10 older women participating in the WHI reported using the internet to obtain health information. Patients recently diagnosed with cancer are more likely to be looking for health information online, even after adjustment for age, suggesting that these patients may have a greater need for digital health resources. ",
doi="10.2196/15906",
url="http://www.jmir.org/2020/4/e15906/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32271152"
}


@Article{info:doi/10.2196/14979,
author="Demergazzi, Silvia
and Pastore, Luca
and Bassani, Giada
and Arosio, Marco
and Lonati, Caterina",
title="Information Needs and Information-Seeking Behavior of Italian Neurologists: Exploratory Mixed Methods Study",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="8",
volume="22",
number="4",
pages="e14979",
keywords="information-seeking behavior",
keywords="information needs",
keywords="information sources",
keywords="medical information delivery",
keywords="neurologists",
keywords="multiple sclerosis",
keywords="migraine",
abstract="Background: Current medical professions involve an extensive knowledge of the latest validated scientific data to implement disease diagnosis, therapeutic strategies, and patient care. Although clinicians can refer to a growing number and type of information sources to keep current with new scientific achievements, there are still various concerns about medical information validity, quality, and applicability into clinical practice. Novel strategies are required to identify physicians' real-life needs with the final aim to improve modern medical information delivery. Objective: Our research used an innovative tool to collect real-time physician queries in order to investigate information needs and seeking behavior of Italian neurologists treating patients with multiple sclerosis (MS) and migraine. Methods: The study was designed as an exploratory mixed methods (ie, qualitative and quantitative) study involving 15 consecutive days of observation. A total of 50 neurologists (n=25 MS and n=25 migraine specialists) were recruited. Data were collected using an instant messaging mobile app designed for this research. At each information-seeking event, moderators triggered a computer-assisted personal interview including both semistructured interview and close-ended questions. Interactions and physician queries collected using the mobile app were coded into emerging themes by content analysis. Results: Neurologist queries were relevant to the following major themes: therapy management (36/50, 71\%) and drug-related information (34/50, 67\%), followed by diagnostic strategies and procedures (21/50, 42\%). Quantitative analysis indicated online resources were preferentially used by clinicians (48/50, 96\%) compared with offline sources (24/50, 47\%). A multichannel approach, in which both online and offline sources were consulted to meet the same need, was adopted in 33\% (65/198) of information-seeking events. Neurologists more likely retrieved information from online relative to offline channels (F=1.7; P=.01). MS specialists were 53\% more likely to engage in one information-seeking event compared with migraine neurologists (risk ratio 1.54; 95\% CI 1.16-2.05). MS specialists tended to be more interested in patient-related content than migraine clinicians (28\% [7/25] vs 10\% [2/25], P=.06), who conversely more likely sought information concerning therapy management (85\% [21/25] vs 60\% [15/25], P=.05). Compared with MS clinicians, migraine specialists had a harder time finding the required information, either looking at online or offline channels (F=12.5; P=.01) and less frequently used offline channels (30\% [8/25] vs 60\% [15/25] of information-seeking events, P=.02). When multiple sources needed to be consulted to retrieve an information item, a reduced satisfaction rate was observed both among migraine and MS specialists (single source vs multiple sources P=.003). Conclusions: This study provides a detailed description of real-life seeking behavior, educational needs, and information sources adopted by Italian MS and migraine neurologists. Neurologist information needs and seeking behavior reflect the specific characteristics of the specialty area in which they operate. These findings suggest identification of time- and context-specific needs of clinicians is required to design an effective medical information strategy. ",
doi="10.2196/14979",
url="https://www.jmir.org/2020/4/e14979",
url="http://www.ncbi.nlm.nih.gov/pubmed/32181742"
}


@Article{info:doi/10.2196/16606,
author="Schoeb, Dominik
and Suarez-Ibarrola, Rodrigo
and Hein, Simon
and Dressler, Friedrich Franz
and Adams, Fabian
and Schlager, Daniel
and Miernik, Arkadiusz",
title="Use of Artificial Intelligence for Medical Literature Search: Randomized Controlled Trial Using the Hackathon Format",
journal="Interact J Med Res",
year="2020",
month="Mar",
day="30",
volume="9",
number="1",
pages="e16606",
keywords="artificial intelligence",
keywords="literature review",
keywords="medical information technology",
abstract="Background: Mapping out the research landscape around a project is often time consuming and difficult. Objective: This study evaluates a commercial artificial intelligence (AI) search engine (IRIS.AI) for its applicability in an automated literature search on a specific medical topic. Methods: To evaluate the AI search engine in a standardized manner, the concept of a science hackathon was applied. Three groups of researchers were tasked with performing a literature search on a clearly defined scientific project. All participants had a high level of expertise for this specific field of research. Two groups were given access to the AI search engine IRIS.AI. All groups were given the same amount of time for their search and were instructed to document their results. Search results were summarized and ranked according to a predetermined scoring system. Results: The final scoring awarded 49 and 39 points out of 60 to AI groups 1 and 2, respectively, and the control group received 46 points. A total of 20 scientific studies with high relevance were identified, and 5 highly relevant studies (``spot on'') were reported by each group. Conclusions: AI technology is a promising approach to facilitate literature searches and the management of medical libraries. In this study, however, the application of AI technology lead to a more focused literature search without a significant improvement in the number of results. ",
doi="10.2196/16606",
url="http://www.i-jmr.org/2020/1/e16606/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32224481"
}


@Article{info:doi/10.2196/14414,
author="Alduraywish, A. Shatha
and Altamimi, A. Lamees
and Aldhuwayhi, A. Rawan
and AlZamil, R. Lama
and Alzeghayer, Y. Luluh
and Alsaleh, S. Futoon
and Aldakheel, M. Fahad
and Tharkar, Shabana",
title="Sources of Health Information and Their Impacts on Medical Knowledge Perception Among the Saudi Arabian Population: Cross-Sectional Study",
journal="J Med Internet Res",
year="2020",
month="Mar",
day="19",
volume="22",
number="3",
pages="e14414",
keywords="health information sources",
keywords="health perception",
keywords="medical information sources",
keywords="satisfaction",
keywords="social media",
keywords="trust",
abstract="Background: Having a reliable source for health information is vital to build a strong foundation of knowledge, especially with the current revolution of the internet and social media, which raises many concerns regarding harmful effects on the health of the public. However, there are no studies on how the Saudi Arabian population seeks health information. Details about the most used and trusted sources of health information among the public will help health authorities and public awareness accounts on social media to effectively disseminate health information. Objective: To investigate the types of sources accessed by the Saudi Arabian population while seeking health information, as well as their level of trust in the sources and to assess the impact of these sources on their perception of medical knowledge and health decision-making. Methods: A cross-sectional study was conducted to meet the objectives. The study population included both men and women who were aged 16 years or more and visited primary care clinics at King Khalid University Hospital. Four hundred and thirteen participants were sampled using the simple random method, and a self-administered questionnaire was used to collect data. The data were analyzed using SPSS software (IBM Corp, Armonk, New York, USA). Results: A total of 413 participants were included in this study, and of these, 99 (24.0\%) were males and 206 (49.9\%) had a bachelor's degree. Doctors were chosen as the first source of information by 87.6\% (283/323) of the participants, and they were completely trusted by most of the population (326/411, 79.3\%). The second most commonly used source was pharmacists (112/194, 57.7\%), and they were partially trusted by 41.4\% (159/384) of the participants. Internet searches, social media, and traditional medicine were not prioritized by most of the participants as the first or second source of health information. The majority of the participants did not trust information obtained from social media, and WhatsApp was the most untrusted source. Almost half of the respondents (197/413, 47.7\%) acknowledged that various sources of information can often help them understand their health problems. However, the majority disagreed on substituting a doctor's prescription with information obtained from the internet or a friend or relative. Conclusions: Although physicians were preferred and highly trusted, internet sources appeared to impact the medical knowledge of the population. The population still preferred to use internet search to obtain health information prior to a doctor's visit. ",
doi="10.2196/14414",
url="http://www.jmir.org/2020/3/e14414/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32191208"
}


@Article{info:doi/10.2196/16133,
author="Zigdon, Avi
and Zigdon, Tamar
and Moran, Sender Daniel",
title="Attitudes of Nurses Towards Searching Online for Medical Information for Personal Health Needs: Cross-Sectional Questionnaire Study",
journal="J Med Internet Res",
year="2020",
month="Mar",
day="16",
volume="22",
number="3",
pages="e16133",
keywords="information retrieval",
keywords="social media",
keywords="evidence-based practice",
keywords="nursing education",
keywords="eHealth",
abstract="Background: Use of online clinical health care information has become part of the skill set required by medical teams. Nurses believe that information quality and availability affect nursing care and methods. However, nurses tend not to exploit professional medical databases for evidence-based medical information for their personal needs. This phenomenon has received little research attention. Objective: This study aimed to address the knowledge gap around nurses' attitudes towards searching online for medical information for their personal needs (ie, for themselves and their families) by (1) evaluating the level of exposure to medical information and the effect on attitudes towards the use of online search options, (2) assessing the effect of the choice of a primary means of searching for medical information on the attitudes towards the use of online search options, and (3) gauging the influence of sociodemographic data and health status on nurses' attitudes towards searching online for medical information. Methods: Nurses employed in general departments in a general hospital (34/210, 16.2\%), nursing home (42/200, 21.0\%), and geriatric medical center (45/180, 25.0\%) in Israel were invited to complete the eHealth Impact Questionnaire (alpha=.95). Questionnaires were distributed by nurses in charge of the general hospitalization wards. The data collection period was February to March 2018. The response rate was 40.3\% (121/300). Results: Nurses tended to search for medical information for personal needs on social media (24/121, 19.8\%) and TV (eg, health programs, health news; 23/121, 19.0\%). Nurses who chose social media as their primary means of receiving general information had a positive attitude about using the online environment as a source for medical information compared to nurses who found information through other means (t119=4.44, P<.001). Nurses exposed to medical information via social media had a positive attitude towards the use of the internet to find medical information compared to nurses who were not exposed to social media (t119=3.04, P=.003). The attitudes of nurses towards the utility of online medical information for personal needs increased with better participant health status (F2,118=3.63, P=.03). However, the attitudes of participants with a chronic disease did not differ from those of healthy participants. Conclusions: Nurses in Israel are less likely to use their professional skills and knowledge to search in professional databases for evidence-based medical information for their personal needs. Instead, they prefer medical information that is easy to access and not evidence-based, such as that on social media and TV. However, these search patterns for personal use may affect their clinical role, impair quality of care, and lead to incorrect medical decisions for their patients in the health care system. Therefore, during nursing education, training for searching skills, retrieval skills, and online search techniques for evidence-based medical information is vital for evidence-based practice. ",
doi="10.2196/16133",
url="http://www.jmir.org/2020/3/e16133/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32175910"
}


@Article{info:doi/10.2196/15065,
author="Hochberg, Irit
and Allon, Raviv
and Yom-Tov, Elad",
title="Assessment of the Frequency of Online Searches for Symptoms Before Diagnosis: Analysis of Archival Data",
journal="J Med Internet Res",
year="2020",
month="Mar",
day="6",
volume="22",
number="3",
pages="e15065",
keywords="search engines",
keywords="diagnosis",
keywords="screening",
abstract="Background: Surveys suggest that a large proportion of people use the internet to search for information on medical symptoms they experience and that around one-third of the people in the United States self-diagnose using online information. However, surveys are known to be biased, and the true rates at which people search for information on their medical symptoms before receiving a formal medical diagnosis are unknown. Objective: This study aimed to estimate the rate at which people search for information on their medical symptoms before receiving a formal medical diagnosis by a health professional. Methods: We collected queries made on a general-purpose internet search engine by people in the United States who self-identified their diagnosis from 1 of 20 medical conditions. We focused on conditions that have evident symptoms and are neither screened systematically nor a part of usual medical care. Thus, they are generally diagnosed after the investigation of specific symptoms. We evaluated how many of these people queried for symptoms associated with their medical condition before their formal diagnosis. In addition, we used a survey questionnaire to assess the familiarity of laypeople with the symptoms associated with these conditions. Results: On average, 15.49\% (1792/12,367, SD 8.4\%) of people queried about symptoms associated with their medical condition before receiving a medical diagnosis. A longer duration between the first query for a symptom and the corresponding diagnosis was correlated with an increased likelihood of people querying about those symptoms (rho=0.6; P=.005); similarly, unfamiliarity with the association between a condition and its symptom was correlated with an increased likelihood of people querying about those symptoms (rho=?0.47; P=.08). In addition, worrying symptoms were 14\% more likely to be queried about. Conclusions: Our results indicate that there is large variability in the percentage of people who query the internet for their symptoms before a formal medical diagnosis is made. This finding has important implications for systems that attempt to screen for medical conditions. ",
doi="10.2196/15065",
url="https://www.jmir.org/2020/3/e15065",
url="http://www.ncbi.nlm.nih.gov/pubmed/32141835"
}


@Article{info:doi/10.2196/14095,
author="Chung, Kyungmi
and Cho, Young Hee
and Kim, Ran Young
and Jhung, Kyungun
and Koo, Seon Hwa
and Park, Young Jin",
title="Medical Help-Seeking Strategies for Perinatal Women With Obstetric and Mental Health Problems and Changes in Medical Decision Making Based on Online Health Information: Path Analysis",
journal="J Med Internet Res",
year="2020",
month="Mar",
day="4",
volume="22",
number="3",
pages="e14095",
keywords="perinatal care",
keywords="obstetrics",
keywords="mental health",
keywords="information seeking behavior",
keywords="help-seeking behavior",
keywords="self efficacy",
keywords="health literacy",
keywords="consultation",
keywords="decision making",
keywords="internet",
abstract="Background: Previous studies have revealed that most pregnant women rarely discuss informal information found on the internet with health professionals and have frequently expressed concerns for medical experts' reactions to the online information they shared, as well as the lack of time to consult the medical experts in general. To date, little information is available on the effect of individual differences in utilizing medical help-seeking strategies on their medical decisions during the perinatal period. Objective: The objectives of this study were (1) to determine associations among perinatal women's medical help-seeking strategies, changes in medical decision making, and online health information utilization with a focus on the mediating effect of self-efficacy in perinatal health literacy on the intent to consult health professionals, and (2) to clarify these associations in perinatal women with two different medical problems: obstetric and mental health. Methods: A total of 164 perinatal women aged 24 to 47 years (mean 34.64, SD 3.80) repeatedly completed the Problem Solving in Medicine and Online Health Information Utilization questionnaires to examine the moderating effect of two types of medical problems on their decision-making processes. To validate the hypothesized relationships in the proposed conceptual model encompassing obstetric and mental health problem-solving models, path analyses were performed. Results: This study found that some perinatal women, who use an online informal medical help-seeking (OIMH) strategy, would be more likely to change their medical decisions based only on internet-based information without consulting health professionals (P<.001), compared to other women using different medical help-seeking strategies. Particularly, this concern is significantly prevalent when encountering obstetric problems during the perinatal period (obstetric problem-solving: P<.001; mental health problem-solving: P=.02). Furthermore, perinatal women with mental health issues using the OIMH strategy showed a significant difference in intent to consult health professionals based on online health information when the medical problem they had to solve was different (obstetric problem-solving: P=.94; mental health problem-solving: P=.003). Conclusions: Despite the positive mediating effects of perinatal women's enhanced health literacy on the intent to discuss personal medical issues with health professionals based on online health information, the strategy used is of fundamental importance for understanding their help-seeking and decision-making processes during the perinatal period. Beyond a short consultation to steer patients quickly and authoritatively towards an obstetric doctor's choice of action, it is recommended in this study that obstetricians attempt to provide their patients with needed context for the information found online. To fully explain this information with an open mind, they should actively develop or support information and communications technology (ICT)-based health information services. ",
doi="10.2196/14095",
url="https://www.jmir.org/2020/3/e14095",
url="http://www.ncbi.nlm.nih.gov/pubmed/32130139"
}


@Article{info:doi/10.2196/16279,
author="Loda, Teresa
and Erschens, Rebecca
and Junne, Florian
and Stengel, Andreas
and Zipfel, Stephan
and Herrmann-Werner, Anne",
title="Undergraduate Medical Students' Search for Health Information Online: Explanatory Cross-Sectional Study",
journal="JMIR Med Inform",
year="2020",
month="Mar",
day="2",
volume="8",
number="3",
pages="e16279",
keywords="digital health literacy",
keywords="medical education",
keywords="evidence-based online information",
keywords="digital native",
keywords="trustworthy webpages",
abstract="Background: Previous research shows that being a ``digital native'' or growing up in a digital environment does not necessarily lead to increased digital competencies, such as digital health literacy or evaluation of webpage quality. Objective: This study showed how medical students searched for health information online, specifically the recommended testing for histamine intolerance, by comparing the use of various search engines (Google, Medisuch, and a website of the student's choice) to find out more about search strategies in future health professionals. As Medisuch presents a qualitatively better search engine, we assumed that medical students using this search engine might find valid information faster on more reliable webpages, and might recommend the correct diagnostic steps for histamine intolerance to their patients more often than students using a generic search engine like Google. Methods: Medical students in their third year of study were asked to find the relevant diagnostic steps of histamine intolerance online. They were randomly assigned to use one search engine: Google, their personal choice, or Medisuch. Their process of seeking information online was video recorded. Results: In total, 140 medical students participated in this study. The total number of webpages found did not differ among the groups (P=.52). Students using Medisuch (P=.02) correctly identified the elimination diet as a relevant diagnostic step more frequently. The provocation test was reported by almost half of the students independent of the search engine used. In general, medical students commonly identified trustworthy webpages in all three groups (Google: 36/44, 82\%; free choice: 31/36; 86\%; and Medisuch: 35/45, 78\%). Conclusions: The results indicate that medical students were able to find trustworthy health-related information online independent of the search engine used. Medical students that are digital natives seem to have proper internet skills and a knowledge of how to use them. They entered specific medical terms (evidence-based diagnostic steps) or names of reliable webpages (DocCheck) in the search engines to gain correct information. However, it remains to be seen if this behavior can be called true ``digital literacy''. ",
doi="10.2196/16279",
url="https://medinform.jmir.org/2020/3/e16279",
url="http://www.ncbi.nlm.nih.gov/pubmed/32130146"
}


@Article{info:doi/10.2196/15148,
author="Aoun, Lydia
and Lakkis, Najla
and Antoun, Jumana",
title="Prevalence and Outcomes of Web-Based Health Information Seeking for Acute Symptoms: Cross-Sectional Study",
journal="J Med Internet Res",
year="2020",
month="Jan",
day="10",
volume="22",
number="1",
pages="e15148",
keywords="internet",
keywords="health information",
keywords="acute symptoms",
keywords="acute disease",
abstract="Background: The literature indicates that Web-based health information seeking is mostly used for seeking information on well-established diseases. However, only a few studies report health information seeking in the absence of a doctor's visit and in the context of acute symptoms. Objective: This survey aimed to estimate the prevalence of Web-based health information seeking for acute symptoms and the impact of such information on symptom management and health service utilization. Methods: This was a cross-sectional study of a convenience sample of 287 Lebanese adults (with a response rate of 18.5\% [54/291]) conducted between December 2016 and June 2017. The survey was answered by participants online or through phone-based interviews. Results: A total of 64.3\% of the participants (178/277) reported checking the internet for health information when they had an acute symptom. The rate of those who sought to use Web-based health information first when experiencing acute symptom(s) in the past 12 months was 19.2\% (25/130). In addition, 50\% (9/18) visited the doctor because of the obtained information, and the rest self-medicated or sought a pharmacist's advice; the majority (18/24, 75\%) improved within 3-4 days. Conclusions: Higher education level and trust in Web-based medical information were two major predictors of Web-based health information seeking for acute symptoms. Seeking Web-based health information first for acute symptoms is common and may lead to self-management by avoiding a visit to the physician. Physicians should encourage their patients to discuss Web-based health information and guide them toward trusted online websites. ",
doi="10.2196/15148",
url="https://www.jmir.org/2020/1/e15148",
url="http://www.ncbi.nlm.nih.gov/pubmed/31922490"
}


@Article{info:doi/10.2196/14554,
author="Vehof, Hans
and Heerdink, Eibert
and Sanders, Jos{\'e}
and Das, Enny",
title="Associations Between Characteristics of Web-Based Diabetes News and Readers' Sentiments: Observational Study in the Netherlands",
journal="J Med Internet Res",
year="2019",
month="Nov",
day="13",
volume="21",
number="11",
pages="e14554",
keywords="medical journalism",
keywords="diabetes mellitus",
keywords="information seeking behaviors",
keywords="news",
keywords="diffusion of innovation",
abstract="Background: Although experts agree that Web-based health information often contains exaggeration and misrepresentation of science, it is not yet known how this information affects the readers' sentiments. Objective: This study aimed to investigate whether specific aspects of Web-based diabetes research news are associated with positive or negative sentiments in readers. Methods: A retrospective observational study of the comments on diabetes research news posted on Facebook pages was conducted as a function of the innovations' developmental phase, the intended treatment effect, and the use of strong language to intensify the news messages (superlatives). Data for the investigation were drawn from the diabetes research news posted between January 2014 and January 2018 on the two largest Dutch Facebook pages on diabetes and the corresponding reader comments. By manually coding these Facebook user comments, three binary outcome variables were created, reflecting the presence of a positive sentiment, the presence of a negative sentiment, and the presence of a statement expressing hopefulness. Results: Facebook users made a total of 3710 comments on 173 diabetes research news posts that were eligible for further analysis. Facebook user comments on posts about diabetes prevention (odds ratio [OR] 0.55, 95\% CI 0.37-0.84), improved blood glucose regulation (OR 0.68, 95\% CI 0.56-0.84), and symptom relief (OR 0.31, 95\% CI 0.21-0.44) were associated with less positive sentiments as compared with potential diabetes cures. Furthermore, comments on innovations supported by preclinical evidence in animals were associated with more positive sentiments (OR 1.46, 95\% CI 1.07-1.99) and statements expressing hope (OR 1.47, 95\% CI 1.01-2.14), when compared with innovations that have evidence from large human trials. This study found no evidence for the associations between language intensification of the news posts and the readers' sentiments. Conclusions: Our finding that the attitudes toward diabetes research news on Facebook are most positive when clinical efficacy is not (or not yet) proven in large patient trials suggests that news authors and editors, as well as medical professionals, must exercise caution when acting as a conduit for diabetes research news. ",
doi="10.2196/14554",
url="https://www.jmir.org/2019/11/e14554",
url="http://www.ncbi.nlm.nih.gov/pubmed/31719025"
}


@Article{info:doi/10.2196/12278,
author="Kwakernaak, Joyce
and Eekhof, H. Just A.
and De Waal, M. Margot W.
and Barenbrug, M. Elisabeth A.
and Chavannes, H. Niels",
title="Patients' Use of the Internet to Find Reliable Medical Information About Minor Ailments: Vignette-Based Experimental Study",
journal="J Med Internet Res",
year="2019",
month="Nov",
day="11",
volume="21",
number="11",
pages="e12278",
keywords="internet",
keywords="information seeking behaviour",
keywords="consumer health information",
keywords="diagnosis",
keywords="humans",
keywords="adult",
abstract="Background: Little is known about the exact process of how patients search for medical information on the internet and what they retrieve. There is especially a paucity of literature on browsing for information on minor ailments, a term used for harmless diseases that are very common in the general population and thus have a significant impact on health care. Objective: This vignette-based experimental study aimed to explore what kind of Web-based search strategies are applied and how search strategies, demographic characteristics, and the quality of the visited websites relate to finding the right diagnosis. Additional goals were to describe how searching on the Web influences one's perception of the severity of the potential diagnosis and whether or not the participants would discuss the information they found on the internet with their doctors. Methods: Out of 1372 survey participants, 355 were randomly sampled, and 155 of them were recruited and assigned to one of four clinical scenarios. Each search term they used was classified as one of three search strategies: (1) hypothesis testing, (2) narrowing within the general hypothesis area, and (3) symptom exploration. The quality of the websites used was determined by using the DISCERN instrument. To compare the diagnostic accuracy of the participants before and after the internet search, a McNemar test was used. Chi-square tests were used to describe which factors are related to the chosen search strategy. A multivariate binary logistic regression model was constructed to predict which factors are related to finding a sound diagnosis after searching the internet for health information. Results: Most participants (65.8\%, 102/155) used the symptom exploration strategy. However, this depends on the assigned scenario (P<.001) and the self-estimated severity score of the symptoms before the internet search (P=.001). A significant relation was found between choosing an accurate diagnosis and age (odds ratio [OR] 0.94, 95\% CI 0.90 to 0.98) and the clinical scenario, as well as the use of high-quality websites (OR 7.49, 95\% CI 1.85 to 30.26). Browsing the internet did not lead to a statistically significant change in participants' beliefs about the severity of the condition (McNemar test, P=.85). Most participants (65\%) shared their retrieved information with their physician and most of them (75\%) received a positive response. Conclusions: Our findings suggest that most patients use a symptom-based approach; however, if patients expect the potential diagnosis to be severe, they tend to use a hypothesis verification strategy more often and are therefore prone to certain forms of bias. In addition, self-diagnosing accuracy is related to younger age, the symptom scenario, and the use of high-quality websites. We should find ways to guide patients toward search strategies and websites that may more likely lead to accurate decision making. ",
doi="10.2196/12278",
url="http://www.jmir.org/2019/11/e12278/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31710304"
}


@Article{info:doi/10.2196/14327,
author="Ashkanani, Hasan
and Asery, Rabab
and Bokubar, Fajer
and AlAli, Noor
and Mubarak, Shahad
and Buabbas, Ali
and Almajran, Abdullah",
title="Web-Based Health Information Seeking Among Students at Kuwait University: Cross-Sectional Survey Study",
journal="JMIR Form Res",
year="2019",
month="Oct",
day="31",
volume="3",
number="4",
pages="e14327",
keywords="Kuwait",
keywords="online",
keywords="health care",
abstract="Background: Owing to the revolution in technology, the internet has become an important aspect of people's lives. Modern technology is enabling people from diverse educational backgrounds to use the internet for several purposes, one of which is health information seeking. Recently, Web-based health information has become more popular among patients all over the world and among the general public. Objective: This study aimed to investigate the use of Web-based health resources among undergraduate students from different faculties at Kuwait University. Methods: The study employed a cross-sectional design with students selected from 8 faculties of Kuwait University, 4 faculties of Literature and 4 faculties of Science. Data were collected using structured questionnaires, and analysis was done using a chi-square test and binary logistic regression to determine the factors associated with seeking health information on the Web. Results: The sample size obtained was 1132 with a response rate of 90.27\% (1132/1254). Overall, the prevalence of students seeking Web-based health information was 92.66\%. (1049/1132) The most significant factors associated with seeking health information on the Web were age, gender, faculty, year of study, primary source of internet, and level of experience with internet use. In total, 90.0\% (325/361) of students who were aged older than 21 years used Web-based health information compared with 82.8\% (275/332) of those who were aged 18 years. In addition, female students showed a higher prevalence (829/934, 88.8\%) of Web-based health information seeking than males (210/270, 77.8\%). Students who majored in faculties of Science were more likely to seek health information than those who majored in faculties of Literature. All the differences found in the study were statistically significant (P<.05). Conclusions: The study concluded that many people use the internet for seeking health information. Sociodemographic factors have a significant association with Web-based health information seeking. Therefore, doctors must educate the public about the health information websites that can be trusted. ",
doi="10.2196/14327",
url="http://formative.jmir.org/2019/4/e14327/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31473592"
}


@Article{info:doi/10.2196/12621,
author="Miranda, Alvet
and Miah, Jahan Shah",
title="Design and Evaluation of a Contextual Model for Information Retrieval From Web-Scale Discovery Services to Improve Evidence-Based Practice by Health Care Practitioners: Mixed Methods Study",
journal="J Med Internet Res",
year="2019",
month="Aug",
day="21",
volume="21",
number="8",
pages="e12621",
keywords="information retrieval",
keywords="design science research",
keywords="Web-scale discovery",
keywords="evidence-based practice",
keywords="libraries, digital",
keywords="artefacts",
abstract="Background: Practicing evidence-based health care is challenging because of overwhelming results presented to practitioners by Google-like Web-scale discovery (WSD) services that index millions of resources while retrieving information based on relevancy algorithms with limited consideration for user information need. Objective: On the basis of the user-oriented theory of information need and following design science principles, this study aimed to develop and evaluate an innovative contextual model for information retrieval from WSD services to improve evidence-based practice (EBP) by health care practitioners. Methods: We identified problems from literature to support real-world requirements for this study. We used design science research methodology to guide artefact design. We iteratively improved prototype of the context model using artificial formative evaluation. We performed naturalistic summative evaluation using convergent interviewing of health care practitioners and content analysis from a confirmatory focus group consisting of health researchers to evaluate the model's validity and utility. Results: The study iteratively designed and applied the context model to a WSD service to meet 5 identified requirements. All 5 health care practitioners interviewed found the artefact satisfied the 5 requirements to successfully evaluate the model as having validity and utility. Content analysis results from the confirmatory focus group mapped top 5 descriptors per requirement to support a true hypothesis that there is significant discussion among participants to justify concluding that the artefact had validity and utility. Conclusions: The context model for WSD satisfied all requirements and was evaluated successfully for information retrieval to improve EBP. Outcomes from this study justify further research into the model. ",
doi="10.2196/12621",
url="http://www.jmir.org/2019/8/e12621/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31436167"
}


@Article{info:doi/10.2196/13315,
author="Maggio, A. Lauren
and Aakre, A. Christopher
and Del Fiol, Guilherme
and Shellum, Jane
and Cook, A. David",
title="Impact of Clinicians' Use of Electronic Knowledge Resources on Clinical and Learning Outcomes: Systematic Review and Meta-Analysis",
journal="J Med Internet Res",
year="2019",
month="Jul",
day="25",
volume="21",
number="7",
pages="e13315",
keywords="medical education",
keywords="information systems",
keywords="educational technology",
keywords="clinical decision support",
keywords="health information technology",
abstract="Background: Clinicians use electronic knowledge resources, such as Micromedex, UpToDate, and Wikipedia, to deliver evidence-based care and engage in point-of-care learning. Despite this use in clinical practice, their impact on patient care and learning outcomes is incompletely understood. A comprehensive synthesis of available evidence regarding the effectiveness of electronic knowledge resources would guide clinicians, health care system administrators, medical educators, and informaticians in making evidence-based decisions about their purchase, implementation, and use. Objective: The aim of this review is to quantify the impact of electronic knowledge resources on clinical and learning outcomes. Methods: We searched MEDLINE, Embase, PsycINFO, and the Cochrane Library for articles published from 1991 to 2017. Two authors independently screened studies for inclusion and extracted outcomes related to knowledge, skills, attitudes, behaviors, patient effects, and cost. We used random-effects meta-analysis to pool standardized mean differences (SMDs) across studies. Results: Of 10,811 studies screened, we identified 25 eligible studies published between 2003 and 2016. A total of 5 studies were randomized trials, 22 involved physicians in practice or training, and 10 reported potential conflicts of interest. A total of 15 studies compared electronic knowledge resources with no intervention. Of these, 7 reported clinician behaviors, with a pooled SMD of 0.47 (95\% CI 0.27 to 0.67; P<.001), and 8 reported objective patient effects with a pooled SMD of 0.19 (95\% CI 0.07 to 0.32; P=.003). Heterogeneity was large (I2>50\%) across studies. When compared with other resources---7 studies, not amenable to meta-analytic pooling---the use of electronic knowledge resources was associated with increased frequency of answering questions and perceived benefits on patient care, with variable impact on time to find an answer. A total of 2 studies compared different implementations of the same electronic knowledge resource. Conclusions: Use of electronic knowledge resources is associated with a positive impact on clinician behaviors and patient effects. We found statistically significant associations between the use of electronic knowledge resources and improved clinician behaviors and patient effects. When compared with other resources, the use of electronic knowledge resources was associated with increased success in answering clinical questions, with variable impact on speed. Comparisons of different implementation strategies of the same electronic knowledge resource suggest that there are benefits from allowing clinicians to choose to access the resource, versus automated display of resource information, and from integrating patient-specific information. A total of 4 studies compared different commercial electronic knowledge resources, with variable results. Resource implementation strategies can significantly influence outcomes but few studies have examined such factors. ",
doi="10.2196/13315",
url="http://www.jmir.org/2019/7/e13315/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31359865"
}


@Article{info:doi/10.2196/14105,
author="Gesser-Edelsburg, Anat
and Abed Elhadi Shahbari, Nour
and Cohen, Ricky
and Mir Halavi, Adva
and Hijazi, Rana
and Paz-Yaakobovitch, Galit
and Birman, Yael",
title="Differences in Perceptions of Health Information Between the Public and Health Care Professionals: Nonprobability Sampling Questionnaire Survey",
journal="J Med Internet Res",
year="2019",
month="Jul",
day="03",
volume="21",
number="7",
pages="e14105",
keywords="health information-seeking",
keywords="reading and understanding",
keywords="quality criteria for health journalists",
keywords="Web-based and newspaper health information sources",
keywords="journalistic articles",
keywords="public healthcare workers and the general public",
abstract="Background: In the new media age, the public searches for information both online and offline. Many studies have examined how the public reads and understands this information but very few investigate how people assess the quality of journalistic articles as opposed to information generated by health professionals. Objective: The aim of this study was to examine how public health care workers (HCWs) and the general public seek, read, and understand health information and to investigate the criteria by which they assess the quality of journalistic articles. Methods: A Web-based nonprobability sampling questionnaire survey was distributed to Israeli HCWs and members of the public via 3 social media outlets: Facebook, WhatsApp, and Instagram. A total of 979 respondents participated in the online survey via the Qualtrics XM platform. Results: The findings indicate that HCWs find academic articles more reliable than do members of the general public (44.4\% and 28.4\%, respectively, P<.001). Within each group, we found disparities between the places where people search for information and the sources they consider reliable. HCWs consider academic articles to be the most reliable, yet these are not their main information sources. In addition, HCWs often use social networks to search for information (18.2\%, P<.001), despite considering them very unreliable (only 2.2\% found them reliable, P<.001). The same paradoxes were found among the general public, where 37.5\% (P<.001) seek information via social networks yet only 8.4\% (P<.001) find them reliable. Out of 6 quality criteria, 4 were important both to HCWs and to the general public. Conclusions: In the new media age where information is accessible to all, the quality of articles about health is of critical importance. It is important that the criteria examined in this research become the norm in health writing for all stakeholders who write about health, whether they are professional journalists or citizen journalists writing in the new media. ",
doi="10.2196/14105",
url="https://www.jmir.org/2019/7/e14105/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31271145"
}


@Article{info:doi/10.2196/13504,
author="Yang, Lin
and Huang, Xiaoshuo
and Li, Jiao",
title="Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR",
journal="J Med Internet Res",
year="2019",
month="May",
day="28",
volume="21",
number="5",
pages="e13504",
keywords="openEHR",
keywords="clinical information model",
keywords="health information interoperability",
keywords="information retrieval",
keywords="probabilistic graphical model",
abstract="Background: Clinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important. Objective: This study aimed to develop a retrieval method to identify CIMs online to represent EHR data. Methods: We proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics. Results: We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering ``diagnostic reports,'' ``problem list,'' ``patients background,'' ``clinical decision,'' etc, as well as models that other retrieval methods could not find, such as ``problems and diagnoses.'' Conclusions: The graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable. ",
doi="10.2196/13504",
url="http://www.jmir.org/2019/5/e13504/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31140433"
}


@Article{info:doi/10.2196/13082,
author="Lebwohl, Benjamin
and Yom-Tov, Elad",
title="Symptoms Prompting Interest in Celiac Disease and the Gluten-Free Diet: Analysis of Internet Search Term Data",
journal="J Med Internet Res",
year="2019",
month="Apr",
day="08",
volume="21",
number="4",
pages="e13082",
keywords="celiac disease",
keywords="gluten",
keywords="epidemiology",
abstract="Background: Celiac disease, a common immune-based disease triggered by gluten, has diverse clinical manifestations, and the relative distribution of symptoms leading to diagnosis has not been well characterized in the population. Objective: This study aimed to use search engine data to identify a set of symptoms and conditions that would identify individuals at elevated likelihood of a subsequent celiac disease diagnosis. We also measured the relative prominence of these search terms before versus after a search related to celiac disease. Methods: We extracted English-language queries submitted to the Bing search engine in the United States and identified those who submitted a new celiac-related query during a 1-month period, without any celiac-related queries in the preceding 9 months. We compared the ratio between the number of times that each symptom or condition was asked in the 14 days preceding the first celiac-related query of each person and the number of searches for that same symptom or condition in the 14 days after the celiac-related query. Results: We identified 90,142 users who made a celiac-related query, of whom 6528 (7\%) exhibited sustained interest, defined as making a query on more than 1 day. Though a variety of symptoms and associated conditions were also queried before a celiac-related query, the maximum area under the receiver operating characteristic curve was 0.53. The symptom most likely to be queried more before than after a celiac-related query was diarrhea (query ratio [QR] 1.28). Extraintestinal symptoms queried before a celiac disease query included headache (QR 1.26), anxiety (QR 1.10), depression (QR 1.03), and attention-deficit hyperactivity disorder (QR 1.64). Conclusions: We found an increase in antecedent searches for symptoms known to be associated with celiac disease, a rise in searches for depression and anxiety, and an increase in symptoms that are associated with celiac disease but may not be reported to health care providers. The protean clinical manifestations of celiac disease are reflected in the diffuse nature of antecedent internet queries of those interested in celiac disease, underscoring the challenge of effective case-finding strategies. ",
doi="10.2196/13082",
url="https://www.jmir.org/2019/4/e13082/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30958273"
}


@Article{info:doi/10.2196/10769,
author="Lander, Bryn
and Balka, Ellen",
title="Exploring How Evidence is Used in Care Through an Organizational Ethnography of Two Teaching Hospitals",
journal="J Med Internet Res",
year="2019",
month="Mar",
day="28",
volume="21",
number="3",
pages="e10769",
keywords="clinical practice guidelines",
keywords="evidence-based medicine",
keywords="mindlines",
keywords="ethnography",
abstract="Background: Numerous published articles show that clinicians do not follow clinical practice guidelines (CPGs). However, a few studies explore what clinicians consider evidence and how they use different forms of evidence in their care decisions. Many of these existing studies occurred before the advent of smartphones and advanced Web-based information retrieval technologies. It is important to understand how these new technologies influence the ways clinicians use evidence in their clinical practice. Mindlines are a concept that explores how clinicians draw on different sources of information (including context, experience, medical training, and evidence) to develop collectively reinforced, internalized tacit guidelines. Objective: The aim of this paper was to explore how evidence is integrated into mindline development and the everyday use of mindlines and evidence in care. Methods: We draw on ethnographic data collected by shadowing internal medicine teams at 2 teaching hospitals. Fieldnotes were tagged by evidence category, teaching and care, and role of the person referencing evidence. Counts of these tags were integrated with fieldnote vignettes and memos. The findings were verified with an advisory council and through member checks. Results: CPGs represent just one of several sources of evidence used when making care decisions. Some forms of evidence were predominately invoked from mindlines, whereas other forms were read to supplement mindlines. The majority of scientific evidence was accessed on the Web, often through smartphones. How evidence was used varied by role. As team members gained experience, they increasingly incorporated evidence into their mindlines. Evidence was often blended together to arrive at shared understandings and approaches to patient care that included ways to filter evidence. Conclusions: This paper outlines one way through which the ethos of evidence-based medicine has been incorporated into the daily work of care. Here, multiple Web-based forms of evidence were mixed with other information. This is different from the way that is often articulated by health administrators and policy makers whereby clinical practice guideline adherence is equated with practicing evidence-based medicine. ",
doi="10.2196/10769",
url="http://www.jmir.org/2019/3/e10769/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30920371"
}


@Article{info:doi/10.2196/12400,
author="Marcu, Afrodita
and Muller, Cecile
and Ream, Emma
and Whitaker, L. Katriina",
title="Online Information-Seeking About Potential Breast Cancer Symptoms: Capturing Online Behavior With an Internet Browsing Tracking Tool",
journal="J Med Internet Res",
year="2019",
month="Feb",
day="06",
volume="21",
number="2",
pages="e12400",
keywords="breast cancer",
keywords="health information",
keywords="internet search",
keywords="online information seeking",
abstract="Background: People engage in health information-seeking online when experiencing unusual or unfamiliar bodily changes. It is not well understood how people consult the internet for health information after the onset of unfamiliar symptoms and before receiving a potential diagnosis and how online information-seeking can help people appraise their symptoms. This lack of evidence may be partly due to methodological limitations in capturing in real time the online information-seeking process. Objective: We explored women's symptom attribution and online health information-seeking in response to a hypothetical and unfamiliar breast change suggestive of cancer (nipple rash). We also aimed to establish the feasibility of capturing in real time the online information-seeking process with a tool designed to track participant online searches and visited websites, the Vizzata browser tracker. Methods: An online survey was completed by 56 cancer-free women (mean age 60.34 [SD 7.73] years) responding to a scenario asking them to imagine noticing a red scaly rash on the nipple. Participants were asked to make symptom attributions when presented with the scenario (T1) and again after seeking information online (T2). The online tracking tool, embedded in the survey, was used to capture in real time participant search terms and accessed websites. Results: The tracking tool captured the search terms and accessed websites of most of the participants (46/56, 82\%). For the rest (10/56, 18\%), there was evidence of engagement in online information-seeking (eg, medical terminology and cancer attribution at T2) despite their searching activity not being recorded. A total of 25 participants considered cancer as a potential cause for the nipple rash at T1, yet only one of these used cancer as a search term. Most participants (40/46, 87\%) used rash-related search terms, particularly nipple rash and rash on nipple. The majority (41/46, 89\%) accessed websites containing breast cancer information, with the National Health Service webpage ``Paget disease of the nipple'' being the most visited one. At T2, after engaging in the internet search task, more participants attributed the nipple rash to breast cancer than at T1 (37/46, 66\% vs 25/46, 45\%), although a small number of participants (6/46) changed from making a cancer attribution at T1 to a noncancer one at T2. Conclusions: Making a cancer attribution for an unfamiliar breast change did not necessarily translate into cancer-termed searches. Equally, not all internet searches led to a cancer attribution. The findings suggest that online information-seeking may not necessarily help women who experience unfamiliar breast cancer symptoms understand their condition. Despite some technical issues, this study showed that it is feasible to use an online browser tracking tool to capture in real time information-seeking about unfamiliar symptoms. ",
doi="10.2196/12400",
url="http://www.jmir.org/2019/2/e12400/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30724741"
}


@Article{info:doi/10.2196/10986,
author="Palotti, Joao
and Zuccon, Guido
and Hanbury, Allan",
title="Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms",
journal="J Med Internet Res",
year="2019",
month="Jan",
day="30",
volume="21",
number="1",
pages="e10986",
keywords="readability",
keywords="literacy",
keywords="comprehension",
keywords="patients",
keywords="machine learning",
abstract="Background: Understandability plays a key role in ensuring that people accessing health information are capable of gaining insights that can assist them with their health concerns and choices. The access to unclear or misleading information has been shown to negatively impact the health decisions of the general public. Objective: The aim of this study was to investigate methods to estimate the understandability of health Web pages and use these to improve the retrieval of information for people seeking health advice on the Web. Methods: Our investigation considered methods to automatically estimate the understandability of health information in Web pages, and it provided a thorough evaluation of these methods using human assessments as well as an analysis of preprocessing factors affecting understandability estimations and associated pitfalls. Furthermore, lessons learned for estimating Web page understandability were applied to the construction of retrieval methods, with specific attention to retrieving information understandable by the general public. Results: We found that machine learning techniques were more suitable to estimate health Web page understandability than traditional readability formulae, which are often used as guidelines and benchmark by health information providers on the Web (larger difference found for Pearson correlation of .602 using gradient boosting regressor compared with .438 using Simple Measure of Gobbledygook Index with the Conference and Labs of the Evaluation Forum eHealth 2015 collection). Conclusions: The findings reported in this paper are important for specialized search services tailored to support the general public in seeking health advice on the Web, as they document and empirically validate state-of-the-art techniques and settings for this domain application. ",
doi="10.2196/10986",
url="http://www.jmir.org/2019/1/e10986/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30698536"
}


@Article{info:doi/10.2196/11182,
author="Grizzle, J. Amy
and Horn, John
and Collins, Carol
and Schneider, Jodi
and Malone, C. Daniel
and Stottlemyer, Britney
and Boyce, David Richard",
title="Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey",
journal="J Med Internet Res",
year="2019",
month="Jan",
day="04",
volume="21",
number="1",
pages="e11182",
keywords="drug interactions",
keywords="drug interaction experts",
keywords="potential drug-drug interactions",
keywords="surveys \emspace",
abstract="Background: Preventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated. Objective: This study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence. Methods: We invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information---(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes. Results: Twenty participants (response rate, 20/70, 29\%) completed the survey. The majority (17/20, 85\%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60\% (12/20) having >10 years' experience. Over half (11/20, 55\%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90\%) and conducted (19/20, 95\%) search strategies without librarian assistance. PubMed (20/20, 100\%) and Google Scholar (11/20, 55\%) were most commonly searched for papers, followed by Google Web Search (7/20, 35\%) and EMBASE (3/20, 15\%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45\%), Micromedex (8/20, 40\%), Drugs@FDA (17/20, 85\%), and DailyMed (13/20, 65\%). Facts and Comparisons was the most commonly used compendia (8/20, 40\%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not. Conclusions: This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one's ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed. ",
doi="10.2196/11182",
url="https://www.jmir.org/2019/1/e11182/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30609981"
}


@Article{info:doi/10.2196/10281,
author="Del Fiol, Guilherme
and Michelson, Matthew
and Iorio, Alfonso
and Cotoi, Chris
and Haynes, Brian R.",
title="A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study",
journal="J Med Internet Res",
year="2018",
month="Jun",
day="25",
volume="20",
number="6",
pages="e10281",
keywords="information retrieval",
keywords="evidence-based medicine",
keywords="deep learning",
keywords="machine learning",
keywords="literature databases",
abstract="Background: A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. Objective: To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. Methods: We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard. Results: The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9\% vs 98.4\%; P<.001); and equivalent recall to McMaster's textword search (96.9\% vs 97.1\%; P=.57) and Clinical Queries Balanced filter (96.9\% vs 97.0\%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6\% vs 22.4\%; P<.001) and McMaster's textword search (34.6\% vs 11.8\%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6\% vs 40.9\%; P<.001). Conclusions: Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis. ",
doi="10.2196/10281",
url="http://www.jmir.org/2018/6/e10281/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29941415"
}


@Article{info:doi/10.2196/jmir.9717,
author="Cartwright, F. Alice
and Karunaratne, Mihiri
and Barr-Walker, Jill
and Johns, E. Nicole
and Upadhyay, D. Ushma",
title="Identifying National Availability of Abortion Care and Distance From Major US Cities: Systematic Online Search",
journal="J Med Internet Res",
year="2018",
month="May",
day="14",
volume="20",
number="5",
pages="e186",
keywords="abortion seekers",
keywords="reproductive health",
keywords="internet",
keywords="access to information",
keywords="information seeking",
keywords="abortion patients",
keywords="reproductive health services",
keywords="information seeking behavior",
abstract="Background: Abortion is a common medical procedure, yet its availability has become more limited across the United States over the past decade. Women who do not know where to go for abortion care may use the internet to find abortion facility information, and there appears to be more online searches for abortion in states with more restrictive abortion laws. While previous studies have examined the distances women must travel to reach an abortion provider, to our knowledge no studies have used a systematic online search to document the geographic locations and services of abortion facilities. Objective: The objective of our study was to describe abortion facilities and services available in the United States from the perspective of a potential patient searching online and to identify US cities where people must travel the farthest to obtain abortion care. Methods: In early 2017, we conducted a systematic online search for abortion facilities in every state and the largest cities in each state. We recorded facility locations, types of abortion services available, and facility gestational limits. We then summarized the frequencies by region and state. If the online information was incomplete or unclear, we called the facility using a mystery shopper method, which simulates the perspective of patients calling for services. We also calculated distance to the closest abortion facility from all US cities with populations of 50,000 or more. Results: We identified 780 facilities through our online search, with the fewest in the Midwest and South. Over 30\% (236/780, 30.3\%) of all facilities advertised the provision of medication abortion services only; this proportion was close to 40\% in the Northeast (89/233, 38.2\%) and West (104/262, 39.7\%). The lowest gestational limit at which services were provided was 12 weeks in Wyoming; the highest was 28 weeks in New Mexico. People in 27 US cities must travel over 100 miles (160 km) to reach an abortion facility; the state with the largest number of such cities is Texas (n=10). Conclusions: Online searches can provide detailed information about the location of abortion facilities and the types of services they provide. However, these facilities are not evenly distributed geographically, and many large US cities do not have an abortion facility. Long distances can push women to seek abortion in later gestations when care is even more limited. ",
doi="10.2196/jmir.9717",
url="http://www.jmir.org/2018/5/e186/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29759954"
}


@Article{info:doi/10.2196/jmir.9144,
author="Becker, Daniela
and Grapendorf, Johannes
and Greving, Hannah
and Sassenberg, Kai",
title="Perceived Threat and Internet Use Predict Intentions to Get Bowel Cancer Screening (Colonoscopy): Longitudinal Questionnaire Study",
journal="J Med Internet Res",
year="2018",
month="Feb",
day="07",
volume="20",
number="2",
pages="e46",
keywords="emotion",
keywords="Internet",
keywords="colonoscopy",
keywords="cancer screening",
abstract="Background: Many people use the Internet for health-related information search, which is known to help regulate their emotional state. However, not much is known yet about how Web-based information search together with negative emotional states (ie, threat of cancer diagnosis) relate to preventive medical treatment decisions (ie, colonoscopy intentions). Objective: The aim of this study was to investigate how frequency of health-related Internet use together with perceived threat of a possible (bowel) cancer diagnosis influences intentions to get a colonoscopy. Previous research has shown that people who experience threat preferentially process positive information in an attempt to downregulate the aversive emotional state. The Internet can facilitate this regulatory strategy through allowing self-directed, unrestricted, and thus biased information search. In the context of threat regarding a possible bowel cancer diagnosis, feelings of threat can still be effectively reduced through cancer screening (ie, colonoscopy). We, therefore, predict that in that particular context, feelings of threat should be related to stronger colonoscopy intentions, and that this relationship should be enhanced for people who use the Internet often. Methods: A longitudinal questionnaire study was conducted among healthy participants who were approaching or just entering the bowel cancer risk group (aged 45-55 years). Perceived threat of a possible (bowel) cancer diagnosis, frequency of health-related Internet use, and intentions to have a colonoscopy were assessed at 2 time points (6-month time lag between the 2 measurement points T1 and T2). Multiple regression analyses were conducted to test whether threat and Internet use at T1 together predicted colonoscopy intentions at T2. Results: In line with our predictions, we found that the threat of a possible (bowel) cancer diagnosis interacted with the frequency of Internet use (both T1) to predict colonoscopy intentions (T2; B=.23, standard error [SE]=0.09, P=.01). For people who used the Internet relatively often (+1 SD), the positive relationship between threat and colonoscopy intentions was significantly stronger (B=.56, SE=0.15, P<.001) compared with participants who used the Internet less often (?1 SD; B=.17, SE=0.09, P=.07). This relationship was unique to Web-based (vs other types of) information search and independent of risk factors (eg, body mass index [BMI] and smoking). Conclusions: The results of this study suggest that health-related Internet use can facilitate emotion-regulatory processes. People who feel threatened by a possible (bowel) cancer diagnosis reported stronger colonoscopy intentions, especially when they used the Internet often. We propose that this is because people who experience threat are more likely to search for and process information that allows them to downregulate their aversive emotional state. In the present case of (bowel) cancer prevention, the most effective way to reduce threat is to get screened. ",
doi="10.2196/jmir.9144",
url="http://www.jmir.org/2018/2/e46/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29415872"
}


@Article{info:doi/10.2196/medinform.8751,
author="Kim, Seongsoon
and Park, Donghyeon
and Choi, Yonghwa
and Lee, Kyubum
and Kim, Byounggun
and Jeon, Minji
and Kim, Jihye
and Tan, Choon Aik
and Kang, Jaewoo",
title="A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis",
journal="JMIR Med Inform",
year="2018",
month="Jan",
day="05",
volume="6",
number="1",
pages="e2",
keywords="machine comprehension",
keywords="biomedical text comprehension",
keywords="deep learning",
keywords="machine comprehension dataset",
abstract="Background: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective: This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods: We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results: The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7\% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22\% on average. Conclusions: In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC\_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC\_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ",
doi="10.2196/medinform.8751",
url="http://medinform.jmir.org/2018/1/e2/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29305341"
}


@Article{info:doi/10.2196/jmir.8354,
author="Pian, Wenjing
and Khoo, SG Christopher
and Chi, Jianxing",
title="Automatic Classification of Users' Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data",
journal="J Med Internet Res",
year="2017",
month="Dec",
day="21",
volume="19",
number="12",
pages="e424",
keywords="information-seeking behavior",
keywords="social media",
keywords="Internet",
keywords="consumer health information",
keywords="medical informatics",
abstract="Background: Users searching for health information on the Internet may be searching for their own health issue, searching for someone else's health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. Objective: The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. Methods: A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user's mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. Results: An analysis of variance (ANOVA) analysis found that users' browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user's type of health information context with a 10-fold cross validation mean accuracy of 84\% (62/74), followed by model 2 at 73\% (54/74) and model 1 at 71\% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users' age, education level, and the urgency of their information need. Conclusions: A user's type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function. ",
doi="10.2196/jmir.8354",
url="http://www.jmir.org/2017/12/e424/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29269342"
}


@Article{info:doi/10.2196/ijmr.7000,
author="Chu, TW Joanna
and Wang, Ping Man
and Shen, Chen
and Viswanath, Kasisomayajula
and Lam, Hing Tai
and Chan, Chee Sophia Siu",
title="How, When and Why People Seek Health Information Online: Qualitative Study in Hong Kong",
journal="Interact J Med Res",
year="2017",
month="Dec",
day="12",
volume="6",
number="2",
pages="e24",
keywords="Internet",
keywords="information seeking behavior",
keywords="consumer health information",
keywords="focus groups",
abstract="Background: The Internet has become an established source for health information. The number of individuals using the Internet to search for health information, ranging from healthy lifestyle advice to treatment and diseases, continues to grow. Scholars have emphasized the need to give greater voice and influence to health consumers. Hong Kong, being one of the most technologically advanced and connected cities in the world, has one of the highest Internet penetration rates in the world. Given the dearth of research in an Asian context, Hong Kong is an excellent platform to study individuals' perceptions (eg, benefits and limitations on seeking health information online and how the information is used) on health information seeking. Objective: The aim of this paper was to study individuals' perceptions on health information seeking and to document their Internet information--seeking behaviors. Methods: Five focus groups (n=49) were conducted from November 2015 to January 2016 with individuals across different age groups (18 years or above). Focus group contents were audiotaped, transcribed, and analyzed using thematic analysis techniques. Results: Older (55+ years) and less educated respondents were less likely to use the Internet to search for health information. Among individuals who obtained health information via the Internet, regardless of the severity of the health issue, the Internet was always the first source for information. Limited doctor consultation time and barriers to accessing professional health services were the main reasons for using the Internet. Convenience and coverage were regarded as the main advantages, whereas credibility and trustworthiness of health information were noted as limitations. The use of Web-based health information varied among individuals; hence, the implications on the doctor-patient relationship were mixed. Conclusions: The prevalent and increasing use of the Internet for health information seeking suggests the need for health care professionals to understand how it can be optimally utilized to improve health outcomes. Strategies for communicating and disseminating credible health information in a form that users can understand and use are essential. Due to the rapid technological and related behavioral changes, online health information seeking and its effects need to be closely monitored. ",
doi="10.2196/ijmr.7000",
url="http://www.i-jmr.org/2017/2/e24/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29233802"
}


@Article{info:doi/10.2196/medinform.7059,
author="Segura Bedmar, Isabel
and Mart{\'i}nez, Paloma
and Carruana Mart{\'i}n, Adri{\'a}n",
title="Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis",
journal="JMIR Med Inform",
year="2017",
month="Dec",
day="01",
volume="5",
number="4",
pages="e48",
keywords="information storage and retrieval",
keywords="semantic indexing",
keywords="Medical Subject Headings",
abstract="Background: Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. Objective: The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. Methods: Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. Results: Our experiments show promising results with an F1 of 69\% on the test dataset. Conclusions: To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time. ",
doi="10.2196/medinform.7059",
url="http://medinform.jmir.org/2017/4/e48/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29196280"
}


@Article{info:doi/10.2196/jmir.7754,
author="Zeng, Yuqun
and Liu, Xusheng
and Wang, Yanshan
and Shen, Feichen
and Liu, Sijia
and Rastegar-Mojarad, Majid
and Wang, Liwei
and Liu, Hongfang",
title="Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches",
journal="J Med Internet Res",
year="2017",
month="Oct",
day="16",
volume="19",
number="10",
pages="e342",
keywords="education materials",
keywords="patients",
keywords="questions",
keywords="recommendation",
keywords="information retrieval",
abstract="Background: Self-management is crucial to diabetes care and providing expert-vetted content for answering patients' questions is crucial in facilitating patient self-management. Objective: The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients. Methods: We compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents. Results: We retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0\%, 62.8\%, and 54.3\%, respectively. Conclusions: This study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients' questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums. ",
doi="10.2196/jmir.7754",
url="http://www.jmir.org/2017/10/e342/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29038097"
}


@Article{info:doi/10.2196/publichealth.8078,
author="Yang, Hongxi
and Li, Shu
and Sun, Li
and Zhang, Xinyu
and Hou, Jie
and Wang, Yaogang",
title="Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform",
journal="JMIR Public Health Surveill",
year="2017",
month="Oct",
day="03",
volume="3",
number="4",
pages="e64",
keywords="lung cancer",
keywords="risk factors",
keywords="particulate matter",
keywords="PM2.5",
keywords="Baidu Index",
keywords="information seeking behavior",
keywords="public awareness",
keywords="China",
abstract="Background: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public's awareness and knowledge of modifiable risk factors of lung cancer for prevention. Objective: The objective of our study was to explore the public's awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term ``lung cancer'' on an Internet big data platform, Baidu. Methods: We collected daily PM2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. Results: In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM2.5 concentration was poor (all r2s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5\% level for each city. Conclusions: The daily average PM2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public's awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately. ",
doi="10.2196/publichealth.8078",
url="https://publichealth.jmir.org/2017/4/e64/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28974484"
}


@Article{info:doi/10.2196/medinform.7680,
author="Russell-Rose, Tony
and Chamberlain, Jon",
title="Expert Search Strategies: The Information Retrieval Practices of Healthcare Information Professionals",
journal="JMIR Med Inform",
year="2017",
month="Oct",
day="02",
volume="5",
number="4",
pages="e33",
keywords="review",
keywords="surveys and questionnaires",
keywords="search engine",
keywords="information management",
keywords="information systems",
abstract="Background: Healthcare information professionals play a key role in closing the knowledge gap between medical research and clinical practice. Their work involves meticulous searching of literature databases using complex search strategies that can consist of hundreds of keywords, operators, and ontology terms. This process is prone to error and can lead to inefficiency and bias if performed incorrectly. Objective: The aim of this study was to investigate the search behavior of healthcare information professionals, uncovering their needs, goals, and requirements for information retrieval systems. Methods: A survey was distributed to healthcare information professionals via professional association email discussion lists. It investigated the search tasks they undertake, their techniques for search strategy formulation, their approaches to evaluating search results, and their preferred functionality for searching library-style databases. The popular literature search system PubMed was then evaluated to determine the extent to which their needs were met. Results: The 107 respondents indicated that their information retrieval process relied on the use of complex, repeatable, and transparent search strategies. On average it took 60 minutes to formulate a search strategy, with a search task taking 4 hours and consisting of 15 strategy lines. Respondents reviewed a median of 175 results per search task, far more than they would ideally like (100). The most desired features of a search system were merging search queries and combining search results. Conclusions: Healthcare information professionals routinely address some of the most challenging information retrieval problems of any profession. However, their needs are not fully supported by current literature search systems and there is demand for improved functionality, in particular regarding the development and management of search strategies. ",
doi="10.2196/medinform.7680",
url="https://medinform.jmir.org/2017/4/e33/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28970190"
}


@Article{info:doi/10.2196/ijmr.5075,
author="Shroff, L. Purvi
and Hayes, W. Rebecca
and Padmanabhan, Pradeep
and Stevenson, D. Michelle",
title="Internet Usage by Parents Prior to Seeking Care at a Pediatric Emergency Department: Observational Study",
journal="Interact J Med Res",
year="2017",
month="Sep",
day="28",
volume="6",
number="2",
pages="e17",
keywords="Internet",
keywords="emergency department",
keywords="decision making",
abstract="Background: Little is known about how parents utilize medical information on the Internet prior to an emergency department (ED) visit. Objective: The objective of the study was to determine the proportion of parents who accessed the Internet for medical information related to their child's illness in the 24 hours prior to an ED visit (IPED), to identify the websites used, and to understand how the content contributed to the decision to visit the ED. Methods: A 40-question interview was conducted with parents presenting to an ED within a freestanding children's hospital. If parents reported IPED, the number and names of websites were documented. Parents indicated the helpfulness of Web-based content using a 100-mm visual analog scale and the degree to which it contributed to the decision to visit the ED using 5-point Likert-type responses. Results: About 11.8 \% (31/262) reported IPED (95\% CI 7.3-5.3). Parents who reported IPED were more likely to have at least some college education (P=.04), higher annual household income (P=.001), and older children (P=.04) than those who did not report IPED. About 35\% (11/31) could not name any websites used. Mean level of helpfulness of Web-based content was 62 mm (standard deviation, SD=25 mm). After Internet use, some parents (29\%, 9/31) were more certain they needed to visit the ED, whereas 19\% (6/31) were less certain. A majority (87\%, 195/224) of parents who used the Internet stated that they would be somewhat likely or very likely to visit a website recommended by a physician. Conclusions: Nearly 1 out of 8 parents presenting to an urban pediatric ED reported using the Internet in the 24 hours prior to the ED visit. Among privately insured, at least one in 5 parents reported using the Internet prior to visiting the ED. Web-based medical information often influences decision making regarding ED utilization. Pediatric providers should provide parents with recommendations for high-quality sources of health information available on the Internet. ",
doi="10.2196/ijmr.5075",
url="http://www.i-jmr.org/2017/2/e17/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28958988"
}


@Article{info:doi/10.2196/jmir.7671,
author="Austin, Johanna
and Hollingshead, Kristy
and Kaye, Jeffrey",
title="Internet Searches and Their Relationship to Cognitive Function in Older Adults: Cross-Sectional Analysis",
journal="J Med Internet Res",
year="2017",
month="Sep",
day="06",
volume="19",
number="9",
pages="e307",
keywords="Internet",
keywords="geriatrics",
keywords="cognition",
keywords="executive function",
abstract="Background: Alzheimer disease (AD) is a very challenging experience for all those affected. Unfortunately, detection of Alzheimer disease in its early stages when clinical treatments may be most effective is challenging, as the clinical evaluations are time-consuming and costly. Recent studies have demonstrated a close relationship between cognitive function and everyday behavior, an avenue of research that holds great promise for the early detection of cognitive decline. One area of behavior that changes with cognitive decline is language use. Multiple groups have demonstrated a close relationship between cognitive function and vocabulary size, verbal fluency, and semantic ability, using conventional in-person cognitive testing. An alternative to this approach which is inherently ecologically valid may be to take advantage of automated computer monitoring software to continually capture and analyze language use while on the computer. Objective: The aim of this study was to understand the relationship between Internet searches as a measure of language and cognitive function in older adults. We hypothesize that individuals with poorer cognitive function will search using fewer unique terms, employ shorter words, and use less obscure words in their searches. Methods: Computer monitoring software (WorkTime, Nestersoft Inc) was used to continuously track the terms people entered while conducting searches in Google, Yahoo, Bing, and Ask.com. For all searches, punctuation, accents, and non-ASCII characters were removed, and the resulting search terms were spell-checked before any analysis. Cognitive function was evaluated as a z-normalized summary score capturing five unique cognitive domains. Linear regression was used to determine the relationship between cognitive function and Internet searches by controlling for variables such as age, sex, and education. Results: Over a 6-month monitoring period, 42 participants (mean age 81 years [SD 10.5], 83\% [35/42] female) conducted 2915 searches using these top search engines. Participants averaged 3.08 words per search (SD 1.6) and 5.77 letters per word (SD 2.2). Individuals with higher cognitive function used more unique terms per search (beta=.39, P=.002) and employed less common terms in their searches (beta=1.39, P=.02). Cognitive function was not significantly associated with the length of the words used in the searches. Conclusions: These results suggest that early decline in cognitive function may be detected from the terms people search for when they use the Internet. By continuously tracking basic aspects of Internet search terms, it may be possible to detect cognitive decline earlier than currently possible, thereby enabling proactive treatment and intervention. ",
doi="10.2196/jmir.7671",
url="http://www.jmir.org/2017/9/e307/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28877864"
}


@Article{info:doi/10.2196/jmir.6855,
author="Ghweeba, Mayada
and Lindenmeyer, Antje
and Shishi, Sobhi
and Abbas, Mostafa
and Waheed, Amani
and Amer, Shaymaa",
title="What Predicts Online Health Information-Seeking Behavior Among Egyptian Adults? A Cross-Sectional Study",
journal="J Med Internet Res",
year="2017",
month="Jun",
day="22",
volume="19",
number="6",
pages="e216",
keywords="Internet",
keywords="information-seeking behavior",
keywords="computer literacy",
keywords="surveys and questionnaires",
keywords="Egypt",
abstract="Background: Over the last decade, the Internet has become an important source of health-related information for a wide range of users worldwide. Yet, little is known about the personal characteristics of Egyptian Internet users who search for online health information (OHI). Objective: The aim of the study was to identify the personal characteristics of Egyptian OHI seekers and to determine any associations between their personal characteristics and their health information-seeking behavior. Methods: ?This cross-sectional questionnaire study was conducted from?June to October 2015.?A Web-based questionnaire was sent to Egyptian users aged 18 years and older (N=1400) of a popular Arabic-language health information website. The questionnaire included (1) demographic characteristics; (2) self-reported general health status; and (3) OHI-seeking?behavior?that included frequency of use, different topics sought, and self-reported impact of obtained OHI on health?behaviors. Data were?analyzed?using descriptive statistics and multiple regression analysis. Results: A total of 490 participants completed the electronic questionnaire with a response rate equivalent to 35.0\% (490/1400). Regarding personal characteristics, 57.1\% (280/490) of participants were females, 63.4\% (311/490) had a university level qualification, and 37.1\% (182/490) had a chronic health problem. The most commonly sought OHI by the participants was nutrition-related. Results of the multiple regression analysis showed that 31.0\% of the variance in frequency of seeking OHI among Egyptian adults can be predicted by personal characteristics. Participants who sought OHI more frequently were likely to be female, of younger age, had higher education levels, and good self-reported general health. Conclusions: Our results provide insights into personal characteristics and OHI-seeking behaviors of Egyptian OHI users. This will contribute to better recognize their needs, highlight ways to increase the availability of appropriate OHI, and may lead to the provision of tools allowing Egyptian OHI users to navigate to the highest-quality health information. ",
doi="10.2196/jmir.6855",
url="http://www.jmir.org/2017/6/e216/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28642216"
}


@Article{info:doi/10.2196/publichealth.7526,
author="Jabson, M. Jennifer
and Patterson, G. Joanne
and Kamen, Charles",
title="Understanding Health Information Seeking on the Internet Among Sexual Minority People: Cross-Sectional Analysis From the Health Information National Trends Survey",
journal="JMIR Public Health Surveill",
year="2017",
month="Jun",
day="19",
volume="3",
number="2",
pages="e39",
keywords="sexual orientation",
keywords="Internet-based health information seeking",
keywords="internet access",
keywords="sexual minority",
keywords="homosexuality",
keywords="bisexuality",
abstract="Background: Individuals who face barriers to health care are more likely to access the Internet to seek health information. Pervasive stigma and heterosexism in the health care setting are barriers to health care for sexual minority people (SMP, ie, lesbian, gay, and bisexual people); therefore, SMP may be more likely to use the Internet as a source of health information compared to heterosexual people. Objective: Currently, there is a dearth of published empirical evidence concerning health information seeking on the Internet among SMP; the current project addresses this gap. Methods: Data from the 2015 Health Information National Trends Survey Food and Drug Administration Cycle were used to describe and summarize health information seeking among SMP (n=105) and heterosexual people (n=3405). Results: Almost all of the SMP in this sample reported having access to the Internet (92.4\%, 97/105). SMP were equally as likely as heterosexual people to seek health information on the Internet (adjusted odds ratio [aOR] 0.94, 95\% CI 0.56-1.66) and to report incidental exposure to health information online (aOR 1.02, 95\% CI 0.66-1.60). SMP were 58\% more likely to watch a health-related video on YouTube than heterosexual people (aOR 1.58, 95\% CI 1.00-2.47). Incidental exposure to health information was associated with seeking health information for oneself (aOR 3.87, 95\% CI 1.16-14.13) and for someone else (aOR 6.30, 95\% CI 2.40-17.82) among SMP. Conclusions: SMP access the Internet at high rates and seek out health information online. Their incidental exposure could be associated with seeking information for self or others. This suggests that online interventions could be valuable for delivering or promoting health information for SMP. ",
doi="10.2196/publichealth.7526",
url="http://publichealth.jmir.org/2017/2/e39/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28630036"
}


@Article{info:doi/10.2196/jmir.7489,
author="Lee, Kenneth
and Hoti, Kreshnik
and Hughes, David Jeffery
and Emmerton, Lynne",
title="Dr Google Is Here to Stay but Health Care Professionals Are Still Valued: An Analysis of Health Care Consumers' Internet Navigation Support Preferences",
journal="J Med Internet Res",
year="2017",
month="Jun",
day="14",
volume="19",
number="6",
pages="e210",
keywords="health care",
keywords="information seeking behavior",
keywords="Internet",
keywords="chronic disease",
keywords="patients",
keywords="surveys",
abstract="Background: The Internet offers great opportunities for consumers to be informed about their health. However, concerns have been raised regarding its impact on the traditional health consumer-health professional relationship. Our recent survey of 400 Australian adults identified that over half of consumers required some form of navigational support in locating appropriate Web-based health information. We propose that support provided by health professionals would be preferred by consumers; this preference is regardless of whether consumers have a need for navigational support. Secondary analysis of the survey dataset is presented here to quantify consumer-reported support preferences and barriers when navigating Web-based health information. Objective: We aimed to quantitatively identify consumers' support preferences for locating Web-based health information and their barriers when navigating Web-based health information. We also aimed to compare such preferences and barriers between consumers identified as needing and not needing support when locating Web-based health information. Methods: Chi-square ($\chi$2) tests identified whether each listed support preference differed between subgroups of consumers classified as needing (n=205, 51.3\%) or not needing (n=195, 48.8\%) navigational support; degree of association, via phi coefficient ($\phi$) tests, were also considered to ascertain the likely practical significance of any differences. This was repeated for each listed barrier. Free-text responses regarding additional support preferences were descriptively analyzed and compared with the quantitative findings to provide a richer understanding of desired support for health information searches. Results: Of the 400 respondents, the most preferred mode of navigational support was involvement of health professionals; this was reported by participants identified as needing and not needing navigational support. While there was a significant difference between groups, the degree of association was small ($\chi$21 [N=400]=13.2; P<.001; $\phi$=.18). Qualitative data from the free-text responses supported consumers' desire for health professional involvement. The two most commonly reported barriers when navigating desired Web-based health information were (1) volume of available information and (2) inconsistency of information between sources; these were reported by participants with and without a need for navigational support. While participants identified with a need for navigational support were more likely to report volume ($\chi$21 [N=387]= 4.40; P=.04; $\phi$=.11) and inconsistency of information ($\chi$21 [N=387]= 16.10, P<.001, $\phi$=.20) as barriers, the degrees of association were small to moderate. Conclusions: Despite concerns in the literature that the popularity of the Internet could compromise the health consumer-health professional relationship, our findings suggest the contrary. Our findings showed that health professionals were found to be the most commonly preferred mode of navigational support, even among consumers classified as not needing navigational support. Further research into how health professionals could assist consumers with Web-based health information seeking could strengthen the health consumer-health professional relationship amidst the growing use of ``Dr Google.'' ",
doi="10.2196/jmir.7489",
url="http://www.jmir.org/2017/6/e210/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28615156"
}


@Article{info:doi/10.2196/jmir.6755,
author="Mueller, Julia
and Jay, Caroline
and Harper, Simon
and Davies, Alan
and Vega, Julio
and Todd, Chris",
title="Web Use for Symptom Appraisal of Physical Health Conditions: A Systematic Review",
journal="J Med Internet Res",
year="2017",
month="Jun",
day="13",
volume="19",
number="6",
pages="e202",
keywords="Online health information",
keywords="health information seeking",
keywords="Internet",
keywords="symptom appraisal",
keywords="Web search",
keywords="search strategies",
abstract="Background: The Web has become an important information source for appraising symptoms. We need to understand the role it currently plays in help seeking and symptom evaluation to leverage its potential to support health care delivery. Objective: The aim was to systematically review the literature currently available on Web use for symptom appraisal. Methods: We searched PubMed, EMBASE, PsycINFO, ACM Digital Library, SCOPUS, and Web of Science for any empirical studies that addressed the use of the Web by lay people to evaluate symptoms for physical conditions. Articles were excluded if they did not meet minimum quality criteria. Study findings were synthesized using a thematic approach. Results: A total of 32 studies were included. Study designs included cross-sectional surveys, qualitative studies, experimental studies, and studies involving website/search engine usage data. Approximately 35\% of adults engage in Web use for symptom appraisal, but this proportion varies between 23\% and 75\% depending on sociodemographic and disease-related factors. Most searches were symptom-based rather than condition-based. Users viewed only the top search results and interacted more with results that mentioned serious conditions. Web use for symptom appraisal appears to impact on the decision to present to health services, communication with health professionals, and anxiety. Conclusions: Web use for symptom appraisal has the potential to influence the timing of help seeking for symptoms and the communication between patients and health care professionals during consultations. However, studies lack suitable comparison groups as well as follow-up of participants over time to determine whether Web use results in health care utilization and diagnosis. Future research should involve longitudinal follow-up so that we can weigh the benefits of Web use for symptom appraisal (eg, reductions in delays to diagnosis) against the disadvantages (eg, unnecessary anxiety and health care use) and relate these to health care costs. ",
doi="10.2196/jmir.6755",
url="http://www.jmir.org/2017/6/e202/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28611017"
}


@Article{info:doi/10.2196/jmir.6336,
author="Mueller, Julia
and Jay, Caroline
and Harper, Simon
and Todd, Chris",
title="The Role of Web-Based Health Information in Help-Seeking Behavior Prior to a Diagnosis of Lung Cancer: A Mixed-Methods Study",
journal="J Med Internet Res",
year="2017",
month="Jun",
day="08",
volume="19",
number="6",
pages="e189",
keywords="help seeking",
keywords="online health information",
keywords="health information seeking",
keywords="lung cancer",
keywords="symptom appraisal",
abstract="Background: Delays to diagnosis in lung cancer can lead to reduced chance of survival, and patients often wait for several months before presenting symptoms. The time between first symptom recognition until diagnosis has been theorized into three intervals: symptom appraisal, help-seeking, and diagnostic interval (here: ``pathway to diagnosis''). Interventions are needed to reduce delays to diagnosis in lung cancer. The Web has become an important lay health information source and could potentially play a role in this pathway to diagnosis. Objective: Our overall aim was to gain a preliminary insight into whether Web-based information plays a role in the pathway to diagnosis in lung cancer in order to assess whether it may be possible to leverage this information source to reduce delays to diagnosis. Methods: Patients diagnosed with lung cancer in the 6 months before study entry completed a survey about whether (and how, if yes) they had used the Web to appraise their condition prior to diagnosis. Based on survey responses, we purposively sampled patients and their next-of-kin for semistructured interviews (24 interviews; 33 participants). Interview data were analyzed qualitatively using Framework Analysis in the context of the pathway to diagnosis model. Results: A total of 113 patients completed the survey (age: mean 67.0, SD 8.8 years). In all, 20.4\% (23/113) reported they or next-of-kin had researched their condition online before the diagnosis. The majority of searches (20/23, 87.0\%) were conducted by or with the help of next-of-kin. Interview results suggest that patients and next-of-kin perceived an impact of the information found online on all three intervals in the time to diagnosis. In the appraisal interval, participants used online information to evaluate symptoms and possible causes. In the help-seeking interval, the Web was used to inform the decision of whether to present to health services. In the diagnostic interval, it was used to evaluate health care professionals' advice, to support requests for further investigation of symptoms, and to understand medical jargon. Within this interval, we identified two distinct subintervals (before/after relevant diagnostic tests were initiated), in which the Web reportedly played different roles. Conclusions: Because only 20.4\% of the sample reported prediagnosis Web searches, it seems the role of the Web before diagnosis of lung cancer is at present still limited, but this proportion is likely to increase in the future, when barriers such as unfamiliarity with technology and unwillingness to be informed about one's own health are likely to decrease. Participants' perceptions suggest that the Web can have an impact on all three intervals in the pathway to diagnosis. Thus, the Web may hold the potential to reduce delays in the diagnostic process, and this should be explored in future research and interventions. Our results also suggest a division of the diagnostic interval into two subintervals may be useful. ",
doi="10.2196/jmir.6336",
url="http://www.jmir.org/2017/6/e189/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28596146"
}


@Article{info:doi/10.2196/jmir.6302,
author="Prescott, Julie
and Mackie, Lynn",
title="``You Sort of Go Down a Rabbit Hole...You're Just Going to Keep on Searching'': A Qualitative Study of Searching Online for Pregnancy-Related Information During Pregnancy",
journal="J Med Internet Res",
year="2017",
month="Jun",
day="2",
volume="19",
number="6",
pages="e194",
keywords="pregnancy",
keywords="information seeking behavior",
keywords="qualitative research",
abstract="Background: The Web is becoming increasingly popular for gaining information on medical or health issues; with women in particular likely to search online for this type of information and support. Despite the increased use of the Web for health-related information, we need to question whether the Web and the ease of seeking health information that it provides leads to more (patient) empowerment. As well as being a time of joy and expectations, pregnancy can be a worrying time for women, especially first time mums-to-be, with unfamiliar experiences and symptoms and concerns for the baby as well as the self. Objective: Our aim was to explore how and why pregnant women use the Web to gain information and support during pregnancy and what they consider a reliable source. Methods: To meet the objectives of the study, a qualitative approach was required to gather information on the experiences of currently pregnant women who use the Web to gain information and support during their pregnancy. Sixteen pregnant women took part in a semistructured interview, either face-to-face or via telephone. The interviews took place from January to March 2016, all participants were from England, and the health professionals are all employed by the National Health Service (NHS). Qualitative analytical procedures were employed using inductive thematic analysis supported by NVivo software (QSR International). Results: Pregnant women found reassurance from the experiences of others. This reassurance resulted in them feeling less alone, as well as enabling them to normalize any symptoms or experiences they were undergoing. The women understood that caution was needed at times while reading the stories of others, acknowledging the potential for extreme cases or worst case scenarios. This is particularly pertinent to the Web, as this wide range of stories may not be as easily accessible if stories where confined to those in a woman's offline social circle. The interviews provide insights into how and why pregnant women search online for information and perhaps more so, support while pregnant. Conclusions: Searching for health information and advice online during pregnancy is viewed as quick, easy, and accessible. The affordances of the Web have provided women the opportunity to go online as a first port of call. Knowing they were not alone and reading the experiences or symptoms of other pregnant women enabled women to normalize their experience and was ultimately reassuring for pregnant women. ",
doi="10.2196/jmir.6302",
url="http://www.jmir.org/2017/19/e194/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28583906"
}


@Article{info:doi/10.2196/jmir.7027,
author="Coffey, T. Nathan
and Cassese, James
and Cai, Xinsheng
and Garfinkel, Steven
and Patel, Drasti
and Jones, Rebecca
and Shaewitz, Dahlia
and Weinstein, A. Ali",
title="Identifying and Understanding the Health Information Experiences and Preferences of Caregivers of Individuals With Either Traumatic Brain Injury, Spinal Cord Injury, or Burn Injury: A Qualitative Investigation",
journal="J Med Internet Res",
year="2017",
month="May",
day="10",
volume="19",
number="5",
pages="e159",
keywords="traumatic brain injury",
keywords="burns",
keywords="spinal cord injuries",
keywords="caregivers",
keywords="health information, consumer",
keywords="qualitative research",
abstract="Background: In order to meet the challenges of caring for an injured person, caregivers need access to health information. However, caregivers often feel that they lack adequate information. Previous studies of caregivers have primarily focused on either their time and emotional burdens or their health outcomes, but the information needs of caregivers have not been thoroughly investigated. Objective: The purpose of this investigation was to identify the preferred sources of health information for caregivers supporting individuals with injuries and to explore how access to this information could be improved. Methods: A total of 32 caregivers participated in semistructured interviews, which were used in order to develop a more in-depth understanding of these caregivers' information needs. Digital audio recordings of the interviews were used for analysis purposes. These audio recordings were analyzed using a thematic analysis or qualitative content analysis. All of participant's interviews were then coded using the qualitative analysis program, Nvivo 10 for Mac (QSR International). Results: The caregivers endorsed similar behaviors and preferences when seeking and accessing health information. Medical professionals were the preferred source of information, while ease of access made the Internet the most common avenue to obtain information. The challenges faced by participants were frequently a result of limited support. In describing an ideal health system, participants expressed interest in a comprehensive care website offering support network resources, instructive services about the injury and caregiving, and injury-specific materials. Conclusions: According to the participants, an ideal health information system would include a comprehensive care website that offered supportive network resources, instructive services about the injury and caregiving, and materials specific to the type of patient injury. ",
doi="10.2196/jmir.7027",
url="http://www.jmir.org/2017/5/e159/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28490418"
}


@Article{info:doi/10.2196/jmir.6579,
author="Rosenblum, Sara
and Yom-Tov, Elad",
title="Seeking Web-Based Information About Attention Deficit Hyperactivity Disorder: Where, What, and When",
journal="J Med Internet Res",
year="2017",
month="Apr",
day="21",
volume="19",
number="4",
pages="e126",
keywords="attention deficit hyperactivity disorder",
keywords="Internet",
keywords="search engine",
keywords="coping behavior",
keywords="parents",
abstract="Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder, prevalent among 2-10\% of the population. Objective: The objective of this study was to describe where, what, and when people search online for topics related to ADHD. Methods: Data were collected from Microsoft's Bing search engine and from the community question and answer site, Yahoo Answers. The questions were analyzed based on keywords and using further statistical methods. Results: Our results revealed that the Internet indeed constitutes a source of information for people searching the topic of ADHD, and that they search for information mostly about ADHD symptoms. Furthermore, individuals personally affected by the disorder made 2.0 more questions about ADHD compared with others. Questions begin when children reach 2 years of age, with an average age of 5.1 years. Most of the websites searched were not specifically related to ADHD and the timing of searches as well as the query content were different among those prediagnosis compared with postdiagnosis. Conclusions: The study results shed light on the features of ADHD-related searches. Thus, they may help improve the Internet as a source of reliable information, and promote improved awareness and knowledge about ADHD as well as quality of life for populations dealing with the complex phenomena of ADHD. ",
doi="10.2196/jmir.6579",
url="http://www.jmir.org/2017/4/e126/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28432038"
}


@Article{info:doi/10.2196/jmir.6739,
author="Schneider, Anne Janina
and Holland, Patrick Christopher",
title="eHealth Search Patterns: A Comparison of Private and Public Health Care Markets Using Online Panel Data",
journal="J Med Internet Res",
year="2017",
month="Apr",
day="13",
volume="19",
number="4",
pages="e117",
keywords="health information management",
keywords="medical informatics",
keywords="information science",
abstract="Background: Patient and consumer access to eHealth information is of crucial importance because of its role in patient-centered medicine and to improve knowledge about general aspects of health and medical topics. Objectives: The objectives were to analyze and compare eHealth search patterns in a private (United States) and a public (United Kingdom) health care market. Methods: A new taxonomy of eHealth websites is proposed to organize the largest eHealth websites. An online measurement framework is developed that provides a precise and detailed measurement system. Online panel data are used to accurately track and analyze detailed search behavior across 100 of the largest eHealth websites in the US and UK health care markets. Results: The health, medical, and lifestyle categories account for approximately 90\% of online activity, and e-pharmacies, social media, and professional categories account for the remaining 10\% of online activity. Overall search penetration of eHealth websites is significantly higher in the private (United States) than the public market (United Kingdom). Almost twice the number of eHealth users in the private market have adopted online search in the health and lifestyle categories and also spend more time per website than those in the public market. The use of medical websites for specific conditions is almost identical in both markets. The allocation of search effort across categories is similar in both the markets. For all categories, the vast majority of eHealth users only access one website within each category. Those that conduct a search of two or more websites display very narrow search patterns. All users spend relatively little time on eHealth, that is, 3-7 minutes per website. Conclusions: The proposed online measurement framework exploits online panel data to provide a powerful and objective method of analyzing and exploring eHealth behavior. The private health care system does appear to have an influence on eHealth search behavior in terms of search penetration and time spent per website in the health and lifestyle categories. Two explanations are offered: (1) the personal incentive of medical costs in the private market incentivizes users to conduct online search; and (2) health care information is more easily accessible through health care professionals in the United Kingdom compared with the United States. However, the use of medical websites is almost identical, suggesting that patients interested in a specific condition have a motivation to search and evaluate health information, irrespective of the health care market. The relatively low level of search in terms of the number of websites accessed and the average time per website raise important questions about the actual level of patient informedness in both the markets. Areas for future research are outlined. ",
doi="10.2196/jmir.6739",
url="http://www.jmir.org/2017/4/e117/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28408362"
}


@Article{info:doi/10.2196/jmir.6668,
author="Baumann, Eva
and Czerwinski, Fabian
and Reifegerste, Doreen",
title="Gender-Specific Determinants and Patterns of Online Health Information Seeking: Results From a Representative German Health Survey",
journal="J Med Internet Res",
year="2017",
month="Apr",
day="04",
volume="19",
number="4",
pages="e92",
keywords="health information seeking",
keywords="social media",
keywords="gender differences",
keywords="frequency of seeking",
keywords="Internet",
abstract="Background: Online health information-seeking behavior (OHISB) is currently a widespread and common behavior that has been described as an important prerequisite of empowerment and health literacy. Although demographic factors such as socioeconomic status (SES), age, and gender have been identified as important determinants of OHISB, research is limited regarding the gender-specific motivational determinants of OHISB and differences between women and men in the use of online resources for health information purposes. Objective: The aim of this study was to identify gender-specific determinants and patterns of OHISB by analyzing data from a representative German sample of adults (N=1728) with special attention to access and frequency of use as well as topics and sources of OHISB. Methods: We employed a 2-step analysis, that is, after exploring differences between users and nonusers of online health information using logistic regression models, we highlighted gender-specific determinants of the frequency of OHISB by applying zero-truncated negative binomial models. Results: Age (odds ratio, OR for females=0.97, 95\% CI 0.96-0.99) and degree of satisfaction with one's general practitioner (GP) (OR for males=0.73, 95\% CI 0.57-0.92) were gender-specific determinants of access to OHISB. Regarding the frequency of OHISB, daily Internet use (incidence rate ratio, IRR=1.67, 95\% CI 1.19-2.33) and a strong interest in health topics (IRR=1.45, 95\% CI 1.19-1.77) were revealed to be more important predictors than SES (IRR for high SES=1.25, 95\% CI 0.91-1.73). Conclusions: Users indicate that the Internet seems to be capable of providing a valuable source of informational support and patient empowerment. Increasing the potential value of the Internet as a source for health literacy and patient empowerment requires need-oriented and gender-specific health communication efforts, media, and information strategies. ",
doi="10.2196/jmir.6668",
url="http://www.jmir.org/2017/4/e92/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28377367"
}


@Article{info:doi/10.2196/medinform.6918,
author="Demelo, Jonathan
and Parsons, Paul
and Sedig, Kamran",
title="Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE",
journal="JMIR Med Inform",
year="2017",
month="Feb",
day="02",
volume="5",
number="1",
pages="e4",
keywords="MEDLINE",
keywords="user-computer interface",
keywords="information storage and retrieval",
keywords="medical informatics",
keywords="PubMed",
abstract="Background: Diverse users need to search health and medical literature to satisfy open-ended goals such as making evidence-based decisions and updating their knowledge. However, doing so is challenging due to at least two major difficulties: (1) articulating information needs using accurate vocabulary and (2) dealing with large document sets returned from searches. Common search interfaces such as PubMed do not provide adequate support for exploratory search tasks. Objective: Our objective was to improve support for exploratory search tasks by combining two strategies in the design of an interactive visual interface by (1) using a formal ontology to help users build domain-specific knowledge and vocabulary and (2) providing multi-stage triaging support to help mitigate the information overload problem. Methods: We developed a Web-based tool, Ontology-Driven Visual Search and Triage Interface for MEDLINE (OVERT-MED), to test our design ideas. We implemented a custom searchable index of MEDLINE, which comprises approximately 25 million document citations. We chose a popular biomedical ontology, the Human Phenotype Ontology (HPO), to test our solution to the vocabulary problem. We implemented multistage triaging support in OVERT-MED, with the aid of interactive visualization techniques, to help users deal with large document sets returned from searches. Results: Formative evaluation suggests that the design features in OVERT-MED are helpful in addressing the two major difficulties described above. Using a formal ontology seems to help users articulate their information needs with more accurate vocabulary. In addition, multistage triaging combined with interactive visualizations shows promise in mitigating the information overload problem. Conclusions: Our strategies appear to be valuable in addressing the two major problems in exploratory search. Although we tested OVERT-MED with a particular ontology and document collection, we anticipate that our strategies can be transferred successfully to other contexts. ",
doi="10.2196/medinform.6918",
url="http://medinform.jmir.org/2017/1/e4/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28153818"
}


@Article{info:doi/10.2196/jmir.5729,
author="Tan, Swee-Lin Sharon
and Goonawardene, Nadee",
title="Internet Health Information Seeking and the Patient-Physician Relationship: A Systematic Review",
journal="J Med Internet Res",
year="2017",
month="Jan",
day="19",
volume="19",
number="1",
pages="e9",
keywords="Internet",
keywords="information seeking",
keywords="physician-patient relations",
keywords="health information",
abstract="Background: With online health information becoming increasingly popular among patients, concerns have been raised about the impact of patients' Internet health information-seeking behavior on their relationship with physicians. Therefore, it is pertinent to understand the influence of online health information on the patient-physician relationship. Objective: Our objective was to systematically review existing research on patients' Internet health information seeking and its influence on the patient-physician relationship. Methods: We systematically searched PubMed and key medical informatics, information systems, and communication science journals covering the period of 2000 to 2015. Empirical articles that were in English were included. We analyzed the content covering themes in 2 broad categories: factors affecting patients' discussion of online findings during consultations and implications for the patient-physician relationship. Results: We identified 18 articles that met the inclusion criteria and the quality requirement for the review. The articles revealed barriers, facilitators, and demographic factors that influence patients' disclosure of online health information during consultations and the different mechanisms patients use to reveal these findings. Our review also showed the mechanisms in which online information could influence patients' relationship with their physicians. Conclusions: Results of this review contribute to the understanding of the patient-physician relationship of Internet-informed patients. Our main findings show that Internet health information seeking can improve the patient-physician relationship depending on whether the patient discusses the information with the physician and on their prior relationship. As patients have better access to health information through the Internet and expect to be more engaged in health decision making, traditional models of the patient-provider relationship and communication strategies must be revisited to adapt to this changing demographic. ",
doi="10.2196/jmir.5729",
url="http://www.jmir.org/2017/1/e9/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28104579"
}


@Article{info:doi/10.2196/jmir.5447,
author="Cutrona, L. Sarah
and Mazor, M. Kathleen
and Agunwamba, A. Amenah
and Valluri, Sruthi
and Wilson, M. Patrick
and Sadasivam, S. Rajani
and Finney Rutten, J. Lila",
title="Health Information Brokers in the General Population: An Analysis of the Health Information National Trends Survey 2013-2014",
journal="J Med Internet Res",
year="2016",
month="Jun",
day="03",
volume="18",
number="6",
pages="e123",
keywords="health information seeking",
keywords="peer communication",
keywords="social network",
keywords="patient self-management",
keywords="health care decision-making",
abstract="Background: Health information exchanged between friends or family members can influence decision making, both for routine health questions and for serious health issues. A health information broker is a person to whom friends and family turn for advice or information on health-related topics. Characteristics and online behaviors of health information brokers have not previously been studied in a national population. Objective: The objective of this study was to examine sociodemographic characteristics, health information seeking behaviors, and other online behaviors among health information brokers. Methods: Data from the Health Information National Trends Survey (2013-2014; n=3142) were used to compare brokers with nonbrokers. Modified Poisson regression was used to examine the relationship between broker status and sociodemographics and online information seeking. Results: Over half (54.8\%) of the respondents were consulted by family or friends for advice or information on health topics (ie, they acted as health information brokers). Brokers represented 54.1\% of respondents earning <\$20,000 yearly and 56.5\% of respondents born outside the United States. Women were more likely to be brokers (PR 1.34, 95\% CI 1.23-1.47) as were those with education past high school (PR 1.42, CI 1.22-1.65). People aged ?75 were less likely to be brokers as compared to respondents aged 35-49 (PR 0.81, CI 0.67-0.99). Brokers used the Internet more frequently for a variety of online behaviors such as seeking health information, creating and sharing online content, and downloading health information onto a mobile device; and also reported greater confidence in obtaining health information online. Conclusions: More than 50\% of adults who responded to this national survey, including those with low income and those born abroad, were providing health information or advice to friends and family. These individuals may prove to be effective targets for initiatives supporting patient engagement and disease management, and may also be well-positioned within their respective social networks to propagate health messages. ",
doi="10.2196/jmir.5447",
url="http://www.jmir.org/2016/6/e123/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27260952"
}


@Article{info:doi/10.2196/jmir.5430,
author="Ludolph, Ramona
and Allam, Ahmed
and Schulz, J. Peter",
title="Manipulating Google's Knowledge Graph Box to Counter Biased Information Processing During an Online Search on Vaccination: Application of a Technological Debiasing Strategy",
journal="J Med Internet Res",
year="2016",
month="Jun",
day="02",
volume="18",
number="6",
pages="e137",
keywords="search engine",
keywords="online health information search",
keywords="vaccination",
keywords="debiasing",
keywords="search behavior",
keywords="health communication",
keywords="information processing",
keywords="information seeking",
abstract="Background: One of people's major motives for going online is the search for health-related information. Most consumers start their search with a general search engine but are unaware of the fact that its sorting and ranking criteria do not mirror information quality. This misconception can lead to distorted search outcomes, especially when the information processing is characterized by heuristic principles and resulting cognitive biases instead of a systematic elaboration. As vaccination opponents are vocal on the Web, the chance of encountering their non?evidence-based views on immunization is high. Therefore, biased information processing in this context can cause subsequent impaired judgment and decision making. A technological debiasing strategy could counter this by changing people's search environment. Objective: This study aims at testing a technological debiasing strategy to reduce the negative effects of biased information processing when using a general search engine on people's vaccination-related knowledge and attitudes. This strategy is to manipulate the content of Google's knowledge graph box, which is integrated in the search interface and provides basic information about the search topic. Methods: A full 3x2 factorial, posttest-only design was employed with availability of basic factual information (comprehensible vs hardly comprehensible vs not present) as the first factor and a warning message as the second factor of experimental manipulation. Outcome variables were the evaluation of the knowledge graph box, vaccination-related knowledge, as well as beliefs and attitudes toward vaccination, as represented by three latent variables emerged from an exploratory factor analysis. Results: Two-way analysis of variance revealed a significant main effect of availability of basic information in the knowledge graph box on participants' vaccination knowledge scores (F2,273=4.86, P=.01), skepticism/fear of vaccination side effects (F2,273=3.5, P=.03), and perceived information quality (F2,273=3.73, P=.02). More specifically, respondents receiving comprehensible information appeared to be more knowledgeable, less skeptical of vaccination, and more critical of information quality compared to participants exposed to hardly comprehensible information. Although, there was no significant interaction effect between the availability of information and the presence of the warning, there was a dominant pattern in which the presence of the warning appeared to have a positive influence on the group receiving comprehensible information while the opposite was true for the groups exposed to hardly comprehensible information and no information at all. Participants evaluated the knowledge graph box as moderately to highly useful, with no significant differences among the experimental groups. Conclusion: Overall, the results suggest that comprehensible information in the knowledge graph box positively affects participants' vaccination-related knowledge and attitudes. A small change in the content retrieval procedure currently used by Google could already make a valuable difference in the pursuit of an unbiased online information search. Further research is needed to gain insights into the knowledge graph box's entire potential. ",
doi="10.2196/jmir.5430",
url="http://www.jmir.org/2016/6/e137/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27255736"
}


@Article{info:doi/10.2196/mental.5169,
author="Eichenberg, Christiane
and Sawyer, Adam",
title="Do Patients Look Up Their Therapists Online? An Exploratory Study Among Patients in Psychotherapy",
journal="JMIR Mental Health",
year="2016",
month="May",
day="26",
volume="3",
number="2",
pages="e22",
keywords="therapist-targeted googling",
keywords="patient-targeted googling",
keywords="Internet",
keywords="patient-therapist relationship",
abstract="Background: The use of the Internet as a source of health information is growing among people who experience mental health difficulties. The increase in Internet use has led to questions about online information-seeking behaviors, for example, how psychotherapists and patients use the Internet to ascertain information about each other. The notion of psychotherapists seeking information about their patients online (patient-targeted googling, PTG) has been identified and explored. However, the idea of patients searching for information online about their psychotherapists (therapist-targeted googling, TTG) and the associated motives and effects on the therapeutic relationship remain unclear. Objective: This study investigated former and current German-speaking psychotherapy patients' behavior and attitudes relating to TTG. In addition, patients' methods of information gathering, motives, and success in searching for information were examined. Furthermore, patients' experiences and perceptions of PTG were explored. Methods:    Overall, 238 former and current psychotherapy patients responded to a new questionnaire specifically designed to assess the frequency, motives, use, and outcomes of TTG as well as experiences and perceptions of PTG. The study sample was a nonrepresentative convenience sample recruited online via several German-speaking therapy platforms and self-help forums. Results: Of the 238 former and current patients who responded, 106 (44.5\%) had obtained information about their therapists; most of them (n=85, 80.2\%) had used the Internet for this. Besides curiosity, motives behind information searches included the desire to get to know the therapist better by attempting to search for both professional and private information. TTG appeared to be associated with phases of therapy in which patients felt that progress was not being made. Patients being treated for personality disorders appear to engage more frequently in TTG (rphi = 0.21; P=.004). In general, however, information about therapists sought for online was often not found. Furthermore, most patients refrained from telling their therapist about their information searches. Conclusions:     Patients appear to engage in TTG to obtain both professional and private information about their psychotherapists. TTG can be viewed as a form of client-initiated disclosure. It is therefore important to include TTG as a subject in therapists' education and also to raise awareness within patient education. This investigation provides the first findings into TTG to begin debate on this subject. ",
doi="10.2196/mental.5169",
url="http://mental.jmir.org/2016/2/e22/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27230433"
}


@Article{info:doi/10.2196/jmir.5369,
author="Kanthawala, Shaheen
and Vermeesch, Amber
and Given, Barbara
and Huh, Jina",
title="Answers to Health Questions: Internet Search Results Versus Online Health Community Responses",
journal="J Med Internet Res",
year="2016",
month="Apr",
day="28",
volume="18",
number="4",
pages="e95",
keywords="health communication",
keywords="online health communities",
keywords="question types classification",
keywords="self-management",
keywords="health-related Internet behavior use",
keywords="risk of misinformation",
keywords="Internet",
keywords="diabetes",
abstract="Background: About 6 million people search for health information on the Internet each day in the United States. Both patients and caregivers search for information about prescribed courses of treatments, unanswered questions after a visit to their providers, or diet and exercise regimens. Past literature has indicated potential challenges around quality in health information available on the Internet. However, diverse information exists on the Internet---ranging from government-initiated webpages to personal blog pages. Yet we do not fully understand the strengths and weaknesses of different types of information available on the Internet. Objective: The objective of this research was to investigate the strengths and challenges of various types of health information available online and to suggest what information sources best fit various question types. Methods: We collected questions posted to and the responses they received from an online diabetes community and classified them according to Rothwell's classification of question types (fact, policy, or value questions). We selected 60 questions (20 each of fact, policy, and value) and the replies the questions received from the community. We then searched for responses to the same questions using a search engine and recorded the Results: Community responses answered more questions than did search results overall. Search results were most effective in answering value questions and least effective in answering policy questions. Community responses answered questions across question types at an equivalent rate, but most answered policy questions and the least answered fact questions. Value questions were most answered by community responses, but some of these answers provided by the community were incorrect. Fact question search results were the most clinically valid. Conclusions: The Internet is a prevalent source of health information for people. The information quality people encounter online can have a large impact on them. We present what kinds of questions people ask online and the advantages and disadvantages of various information sources in getting answers to those questions. This study contributes to addressing people's online health information needs. ",
doi="10.2196/jmir.5369",
url="http://www.jmir.org/2016/4/e95/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27125622"
}


@Article{info:doi/10.2196/jmir.4541,
author="Wang, Kejian
and Wan, Mei
and Wang, Rui-Sheng
and Weng, Zuquan",
title="Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events",
journal="J Med Internet Res",
year="2016",
month="Apr",
day="01",
volume="18",
number="4",
pages="e76",
keywords="Web-based drug repositioning",
keywords="FDA Adverse Event Reporting System",
keywords="FAERS",
keywords="openFDA",
keywords="big data",
keywords="antihypertensive drugs",
keywords="hypotension",
abstract="Background: Drug repositioning refers to the process of developing new indications for existing drugs. As a phenotypic indicator of drug response in humans, clinical side effects may provide straightforward signals and unique opportunities for drug repositioning. Objective: We aimed to identify drugs frequently associated with hypotension adverse reactions (ie, the opposite condition of hypertension), which could be potential candidates as antihypertensive agents. Methods: We systematically searched the electronic records of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) through the openFDA platform to assess the association between hypotension incidence and antihypertensive therapeutic effect regarding a list of 683 drugs. Results: Statistical analysis of FAERS data demonstrated that those drugs frequently co-occurring with hypotension events were more likely to have antihypertensive activity. Ranked by the statistical significance of frequent hypotension reporting, the well-known antihypertensive drugs were effectively distinguished from others (with an area under the receiver operating characteristic curve > 0.80 and a normalized discounted cumulative gain of 0.77). In addition, we found a series of antihypertensive agents (particularly drugs originally developed for treating nervous system diseases) among the drugs with top significant reporting, suggesting the good potential of Web-based and data-driven drug repositioning. Conclusions: We found several candidate agents among the hypotension-related drugs on our list that may be redirected for lowering blood pressure. More important, we showed that a pharmacovigilance system could alternatively be used to identify antihypertensive agents and sustainably create opportunities for drug repositioning. ",
doi="10.2196/jmir.4541",
url="http://www.jmir.org/2016/4/e76/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27036325"
}


@Article{info:doi/10.2196/jmir.4373,
author="Aicken, RH Catherine
and Estcourt, S. Claudia
and Johnson, M. Anne
and Sonnenberg, Pam
and Wellings, Kaye
and Mercer, H. Catherine",
title="Use of the Internet for Sexual Health Among Sexually Experienced Persons Aged 16 to 44 Years: Evidence from a Nationally Representative Survey of the British Population",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="20",
volume="18",
number="1",
pages="e14",
keywords="sexual health",
keywords="sexually transmitted diseases",
keywords="contraception",
keywords="health care-seeking behavior",
keywords="Internet",
keywords="eHealth",
keywords="surveys",
keywords="information-seeking behavior",
abstract="Background: Those who go online regarding their sexual health are potential users of new Internet-based sexual health interventions. Understanding the size and characteristics of this population is important in informing intervention design and delivery. Objective: We aimed to estimate the prevalence in Britain of recent use of the Internet for key sexual health reasons (for chlamydia testing, human immunodeficiency virus [HIV] testing, sexually transmitted infection [STI] treatment, condoms/contraceptives, and help/advice with one's sex life) and to identify associated sociodemographic and behavioral factors. Methods: Complex survey analysis of data from 8926 sexually experienced persons aged 16-44 years in a 2010-2012 probability survey of Britain's resident population. Prevalence of recent (past year) use of Internet sources for key sexual health reasons was estimated. Factors associated with use of information/support websites were identified using logistic regression to calculate age-adjusted odds ratios (AORs). Results: Recent Internet use for chlamydia/HIV testing or STI treatment (combined) was very low (men: 0.31\%; women: 0.16\%), whereas 2.35\% of men and 0.51\% of women reported obtaining condoms/contraceptives online. Additionally, 4.49\% of men and 4.57\% of women reported recent use of information/support websites for advice/help with their sex lives. Prevalence declined with age (men 16-24 years: 7.7\%; 35-44 years: 1.84\%, P<.001; women 16-24 years: 7.8\%; 35-44 years: 1.84\%, P<.001). Use of information/support websites was strongly associated with men's higher socioeconomic status (managerial/professional vs semiroutine/routine: AOR 1.93, 95\% CI 1.27-2.93, P<.001). Despite no overall association with area-level deprivation, those in densely populated urban areas were more likely to report use of information/support websites than those living in rural areas (men: AOR 3.38, 95\% CI 1.68-6.77, P<.001; women: AOR 2.51, 95\% CI 1.34-4.70, P<.001). No statistically significant association was observed with number of sex partners reported after age adjustment, but use was more common among men reporting same-sex partners (last 5 years: AOR 2.44, 95\% CI 1.27-4.70), women reporting sex with multiple partners without condoms (last year: AOR 1.90, 95\% CI 1.11-3.26), and, among both sexes, reporting seeking sex online (last year, men: AOR 1.80, 95\% CI 1.16-2.79; women: AOR 3.00, 95\% CI 1.76-5.13). No association was observed with reporting STI diagnosis/es (last 5 years) or (after age adjustment) recent use of any STI service or non-Internet sexual health seeking. Conclusions: A minority in Britain used the Internet for the sexual health reasons examined. Use of information/support websites was reported by those at greater STI risk, including younger people, indicating that demand for online STI services, and Internet-based sexual health interventions in general, may increase over time in this and subsequent cohorts. However, the impact on health inequalities needs addressing during design and evaluation of online sexual health interventions so that they maximize public health benefit. ",
doi="10.2196/jmir.4373",
url="http://www.jmir.org/2016/1/e14/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26792090"
}


@Article{info:doi/10.2196/jmir.5234,
author="Kwag, Hyogene Koren
and Gonz{\'a}lez-Lorenzo, Marien
and Banzi, Rita
and Bonovas, Stefanos
and Moja, Lorenzo",
title="Providing Doctors With High-Quality Information: An Updated Evaluation of Web-Based Point-of-Care Information Summaries",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="19",
volume="18",
number="1",
pages="e15",
keywords="point-of-care summaries",
keywords="internet information",
keywords="evidence-based medicine",
keywords="information science",
abstract="Background: The complexity of modern practice requires health professionals to be active information-seekers. Objective: Our aim was to review the quality and progress of point-of-care information summaries---Web-based medical compendia that are specifically designed to deliver pre-digested, rapidly accessible, comprehensive, and periodically updated information to health care providers. We aimed to evaluate product claims of being evidence-based. Methods: We updated our previous evaluations by searching Medline, Google, librarian association websites, and conference proceedings from August 2012 to December 2014. We included Web-based, regularly updated point-of-care information summaries with claims of being evidence-based. We extracted data on the general characteristics and content presentation of products, and we quantitatively assessed their breadth of disease coverage, editorial quality, and evidence-based methodology. We assessed potential relationships between these dimensions and compared them with our 2008 assessment. Results: We screened 58 products; 26 met our inclusion criteria. Nearly a quarter (6/26, 23\%) were newly identified in 2014. We accessed and analyzed 23 products for content presentation and quantitative dimensions. Most summaries were developed by major publishers in the United States and the United Kingdom; no products derived from low- and middle-income countries. The main target audience remained physicians, although nurses and physiotherapists were increasingly represented. Best Practice, Dynamed, and UptoDate scored the highest across all dimensions. The majority of products did not excel across all dimensions: we found only a moderate positive correlation between editorial quality and evidence-based methodology (r=.41, P=.0496). However, all dimensions improved from 2008: editorial quality (P=.01), evidence-based methodology (P=.015), and volume of diseases and medical conditions (P<.001). Conclusions: Medical and scientific publishers are investing substantial resources towards the development and maintenance of point-of-care summaries. The number of these products has increased since 2008 along with their quality. Best Practice, Dynamed, and UptoDate scored the highest across all dimensions, while others that were marketed as evidence-based were less reliable. Individuals and institutions should regularly assess the value of point-of-care summaries as their quality changes rapidly over time. ",
doi="10.2196/jmir.5234",
url="http://www.jmir.org/2016/1/e15/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26786976"
}


@Article{info:doi/10.2196/jmir.4912,
author="Jay, Caroline
and Harper, Simon
and Dunlop, Ian
and Smith, Sam
and Sufi, Shoaib
and Goble, Carole
and Buchan, Iain",
title="Natural Language Search Interfaces: Health Data Needs Single-Field Variable Search",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="14",
volume="18",
number="1",
pages="e13",
keywords="searching behavior",
keywords="search engine",
keywords="research data archives",
keywords="user-computer interface",
abstract="Background: Data discovery, particularly the discovery of key variables and their inter-relationships, is key to secondary data analysis, and in-turn, the evolving field of data science. Interface designers have presumed that their users are domain experts, and so they have provided complex interfaces to support these ``experts.'' Such interfaces hark back to a time when searches needed to be accurate first time as there was a high computational cost associated with each search. Our work is part of a governmental research initiative between the medical and social research funding bodies to improve the use of social data in medical research. Objective: The cross-disciplinary nature of data science can make no assumptions regarding the domain expertise of a particular scientist, whose interests may intersect multiple domains. Here we consider the common requirement for scientists to seek archived data for secondary analysis. This has more in common with search needs of the ``Google generation'' than with their single-domain, single-tool forebears. Our study compares a Google-like interface with traditional ways of searching for noncomplex health data in a data archive. Methods: Two user interfaces are evaluated for the same set of tasks in extracting data from surveys stored in the UK Data Archive (UKDA). One interface, Web search, is ``Google-like,'' enabling users to browse, search for, and view metadata about study variables, whereas the other, traditional search, has standard multioption user interface. Results: Using a comprehensive set of tasks with 20 volunteers, we found that the Web search interface met data discovery needs and expectations better than the traditional search. A task {\texttimes} interface repeated measures analysis showed a main effect indicating that answers found through the Web search interface were more likely to be correct (F1,19=37.3, P<.001), with a main effect of task (F3,57=6.3, P<.001). Further, participants completed the task significantly faster using the Web search interface (F1,19=18.0, P<.001). There was also a main effect of task (F2,38=4.1, P=.025, Greenhouse-Geisser correction applied). Overall, participants were asked to rate learnability, ease of use, and satisfaction. Paired mean comparisons showed that the Web search interface received significantly higher ratings than the traditional search interface for learnability (P=.002, 95\% CI [0.6-2.4]), ease of use (P<.001, 95\% CI [1.2-3.2]), and satisfaction (P<.001, 95\% CI [1.8-3.5]). The results show superior cross-domain usability of Web search, which is consistent with its general familiarity and with enabling queries to be refined as the search proceeds, which treats serendipity as part of the refinement. Conclusions: The results provide clear evidence that data science should adopt single-field natural language search interfaces for variable search supporting in particular: query reformulation; data browsing; faceted search; surrogates; relevance feedback; summarization, analytics, and visual presentation. ",
doi="10.2196/jmir.4912",
url="http://www.jmir.org/2016/1/e13/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26769334"
}


@Article{info:doi/10.2196/jmir.4306,
author="Eichenberg, Christiane
and Herzberg, Y. Philipp",
title="Do Therapists Google Their Patients? A Survey Among Psychotherapists",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="05",
volume="18",
number="1",
pages="e3",
keywords="patient-targeted googling (PTG)",
keywords="Internet",
keywords="patient-therapist relationship",
keywords="professional-patient relationship, professional guidelines",
keywords="educational curriculum",
abstract="Background: The increasing use of the Internet and its array of social networks brings new ways for psychotherapists to find out information about their patients, often referred to as patient-targeted googling (PTG). However, this topic has been subject to little empirical research; there has been hardly any attention given to it in Germany and the rest of Europe and it has not been included in ethical guidelines for psychotherapy despite the complex ethical issues it raises. Objective: This study explored German psychotherapists' behavior and experiences related to PTG, investigated how these vary with sociodemographic factors and therapeutic background, and explored the circumstances in which psychotherapists considered PTG to be appropriate or not. Methods: A total of 207 psychotherapists responded to a newly developed questionnaire that assessed their experience of and views on PTG. The study sample was a nonrepresentative convenience sample recruited online via several German-speaking professional therapy platforms. Results: Most therapists (84.5\%, 174/207) stated that they had not actively considered the topic of PTG. However, 39.6\% (82/207) said that they had already looked for patient information online  (eg, when they suspected a patient may have been lying) and 39.3\% (81/207) knew colleagues or supervisors who had done so. Only 2.4\% (5/207) of therapists had come across PTG during their education and training. Conclusions: It is essential to provide PTG as a part of therapists' education and training. Furthermore, the complex problems concerning PTG should be introduced into codes of ethics to provide explicit guidance for psychotherapists in practice. This report provides initial suggestions to open up debate on this topic. ",
doi="10.2196/jmir.4306",
url="http://www.jmir.org/2016/1/e3/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26733210"
}


@Article{info:doi/10.2196/jmir.5239,
author="Bickmore, W. Timothy
and Utami, Dina
and Matsuyama, Robin
and Paasche-Orlow, K. Michael",
title="Improving Access to Online Health Information With Conversational Agents: A Randomized Controlled Experiment",
journal="J Med Internet Res",
year="2016",
month="Jan",
day="04",
volume="18",
number="1",
pages="e1",
keywords="embodied conversational agent",
keywords="search user interface",
keywords="information retrieval user interface",
keywords="Web search",
keywords="health literacy",
keywords="relational agent",
keywords="computer literacy",
keywords="search engine",
keywords="Internet",
abstract="Background: Conventional Web-based search engines may be unusable by individuals with low health literacy for finding health-related information, thus precluding their use by this population. Objective: We describe a conversational search engine interface designed to allow individuals with low health and computer literacy identify and learn about clinical trials on the Internet. Methods: A randomized trial involving 89 participants compared the conversational search engine interface (n=43) to the existing conventional keyword- and facet-based search engine interface (n=46) for the National Cancer Institute Clinical Trials database. Each participant performed 2 tasks: finding a clinical trial for themselves and finding a trial that met prespecified criteria. Results: Results indicated that all participants were more satisfied with the conversational interface based on 7-point self-reported satisfaction ratings (task 1: mean 4.9, SD 1.8 vs mean 3.2, SD 1.8, P<.001; task 2: mean 4.8, SD 1.9 vs mean 3.2, SD 1.7, P<.001) compared to the conventional Web form-based interface. All participants also rated the trials they found as better meeting their search criteria, based on 7-point self-reported scales (task 1: mean 3.7, SD 1.6 vs mean 2.7, SD 1.8, P=.01; task 2: mean 4.8, SD 1.7 vs mean 3.4, SD 1.9, P<.01). Participants with low health literacy failed to find any trials that satisfied the prespecified criteria for task 2 using the conventional search engine interface, whereas 36\% (5/14) were successful at this task using the conversational interface (P=.05). Conclusions: Conversational agents can be used to improve accessibility to Web-based searches in general and clinical trials in particular, and can help decrease recruitment bias against disadvantaged populations. ",
doi="10.2196/jmir.5239",
url="http://www.jmir.org/2016/1/e1/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26728964"
}


@Article{info:doi/10.2196/jmir.4345,
author="Lee, Kenneth
and Hoti, Kreshnik
and Hughes, David Jeffery
and Emmerton, M. Lynne",
title="Consumer Use of ``Dr Google'': A Survey on Health Information-Seeking Behaviors and Navigational Needs",
journal="J Med Internet Res",
year="2015",
month="Dec",
day="29",
volume="17",
number="12",
pages="e288",
keywords="online",
keywords="health information",
keywords="health literacy",
keywords="patient activation",
keywords="information seeking",
keywords="information needs",
keywords="Internet",
keywords="chronic disease",
keywords="patients",
keywords="survey",
abstract="Background: The Internet provides a platform to access health information and support self-management by consumers with chronic health conditions. Despite recognized barriers to accessing Web-based health information, there is a lack of research quantitatively exploring whether consumers report difficulty finding desired health information on the Internet and whether these consumers would like assistance (ie, navigational needs). Understanding navigational needs can provide a basis for interventions guiding consumers to quality Web-based health resources. Objective: We aimed to (1) estimate the proportion of consumers with navigational needs among seekers of Web-based health information with chronic health conditions, (2) describe Web-based health information-seeking behaviors, level of patient activation, and level of eHealth literacy among consumers with navigational needs, and (3) explore variables predicting navigational needs. Methods: A questionnaire was developed based on findings from a qualitative study on Web-based health information-seeking behaviors and navigational needs. This questionnaire also incorporated the eHealth Literacy Scale (eHEALS; a measure of self-perceived eHealth literacy) and PAM-13 (a measure of patient activation). The target population was consumers of Web-based health information with chronic health conditions. We surveyed a sample of 400 Australian adults, with recruitment coordinated by Qualtrics. This sample size was required to estimate the proportion of consumers identified with navigational needs with a precision of 4.9\% either side of the true population value, with 95\% confidence. A subsample was invited to retake the survey after 2 weeks to assess the test-retest reliability of the eHEALS and PAM-13. Results: Of 514 individuals who met our eligibility criteria, 400 (77.8\%) completed the questionnaire and 43 participants completed the retest. Approximately half (51.3\%; 95\% CI 46.4-56.2) of the population was identified with navigational needs. Participants with navigational needs appeared to look for more types of health information on the Internet and from a greater variety of information sources compared to participants without navigational needs. However, participants with navigational needs were significantly less likely to have high levels of eHealth literacy (adjusted odds ratio=0.83, 95\% CI 0.78-0.89, P<.001). Age was also a significant predictor (P=.02). Conclusions: Approximately half of the population of consumers of Web-based health information with chronic health conditions would benefit from support in finding health information on the Internet. Despite the popularity of the Internet as a source of health information, further work is recommended to maximize its potential as a tool to assist self-management in consumers with chronic health conditions. ",
doi="10.2196/jmir.4345",
url="http://www.jmir.org/2015/12/e288/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26715363"
}


@Article{info:doi/10.2196/jmir.5038,
author="Gabarron, Elia
and Lau, YS Annie
and Wynn, Rolf",
title="Is There a Weekly Pattern for Health Searches on Wikipedia and Is the Pattern Unique to Health Topics?",
journal="J Med Internet Res",
year="2015",
month="Dec",
day="22",
volume="17",
number="12",
pages="e286",
keywords="information-seeking behavior",
keywords="health information--seeking behavior",
keywords="periodicity",
keywords="Wikipedia",
keywords="chlamydia",
keywords="gonorrhea",
keywords="HIV",
keywords="AIDS",
keywords="influenza",
keywords="diabetes",
abstract="Background: Online health information--seeking behaviors have been reported to be more common at the beginning of the workweek. This behavior pattern has been interpreted as a kind of ``healthy new start'' or ``fresh start'' due to regrets or attempts to compensate for unhealthy behavior or poor choices made during the weekend. However, the observations regarding the most common health information--seeking day were based only on the analyses of users' behaviors with websites on health or on online health-related searches. We wanted to confirm if this pattern could be found in searches of Wikipedia on health-related topics and also if this search pattern was unique to health-related topics or if it could represent a more general pattern of online information searching---which could be of relevance even beyond the health sector. Objective: The aim was to examine the degree to which the search pattern described previously was specific to health-related information seeking or whether similar patterns could be found in other types of information-seeking behavior. Methods: We extracted the number of searches performed on Wikipedia in the Norwegian language for 911 days for the most common sexually transmitted diseases (chlamydia, gonorrhea, herpes, human immunodeficiency virus [HIV], and acquired immune deficiency syndrome [AIDS]), other health-related topics (influenza, diabetes, and menopause), and 2 nonhealth-related topics (footballer Lionel Messi and pop singer Justin Bieber). The search dates were classified according to the day of the week and ANOVA tests were used to compare the average number of hits per day of the week. Results: The ANOVA tests showed that the sexually transmitted disease queries had their highest peaks on Tuesdays (P<.001) and the fewest searches on Saturdays. The other health topics also showed a weekly pattern, with the highest peaks early in the week and lower numbers on Saturdays (P<.001). Footballer Lionel Messi had the highest mean number of hits on Tuesdays and Wednesdays, whereas pop singer Justin Bieber had the most hits on Tuesdays. Both these tracked search queries also showed significantly lower numbers on Saturdays (P<.001). Conclusions: Our study supports prior studies finding an increase in health information searching at the beginning of the workweek. However, we also found a similar pattern for 2 randomly chosen nonhealth-related terms, which may suggest that the search pattern is not unique to health-related searches. The results are potentially relevant beyond the field of health and our preliminary findings need to be further explored in future studies involving a broader range of nonhealth-related searches. ",
doi="10.2196/jmir.5038",
url="http://www.jmir.org/2015/12/e286/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26693859"
}


@Article{info:doi/10.2196/jmir.4281,
author="Jing, Xia
and Cimino, J. James
and Del Fiol, Guilherme",
title="Usability and Acceptance of the Librarian Infobutton Tailoring Environment: An Open Access Online Knowledge Capture, Management, and Configuration Tool for OpenInfobutton",
journal="J Med Internet Res",
year="2015",
month="Nov",
day="30",
volume="17",
number="11",
pages="e272",
keywords="clinical decision support systems/instrumentation",
keywords="evaluation studies as topic",
keywords="knowledge management tool",
keywords="The Librarian Infobutton Tailoring Environment",
abstract="Background: The Librarian Infobutton Tailoring Environment (LITE) is a Web-based knowledge capture, management, and configuration tool with which users can build profiles used by OpenInfobutton, an open source infobutton manager, to provide electronic health record users with context-relevant links to online knowledge resources. Objective: We conducted a multipart evaluation study to explore users' attitudes and acceptance of LITE and to guide future development. Methods: The evaluation consisted of an initial online survey to all LITE users, followed by an observational study of a subset of users in which evaluators' sessions were recorded while they conducted assigned tasks.  The observational study was followed by administration of a modified System Usability Scale (SUS) survey. Results: Fourteen users responded to the survey and indicated good acceptance of LITE with feedback that was mostly positive. Six users participated in the observational study, demonstrating average task completion time of less than 6 minutes and an average SUS score of 72, which is considered good compared with other SUS scores. Conclusions: LITE can be used to fulfill its designated tasks quickly and successfully. Evaluators proposed suggestions for improvements in LITE functionality and user interface. ",
doi="10.2196/jmir.4281",
url="http://www.jmir.org/2015/11/e272/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26621250"
}


@Article{info:doi/10.2196/jmir.4368,
author="Lee, Ji Young
and Boden-Albala, Bernadette
and Jia, Haomiao
and Wilcox, Adam
and Bakken, Suzanne",
title="The Association Between Online Health Information--Seeking Behaviors and Health Behaviors Among Hispanics in New York City: A Community-Based Cross-Sectional Study",
journal="J Med Internet Res",
year="2015",
month="Nov",
day="26",
volume="17",
number="11",
pages="e261",
keywords="Internet",
keywords="information-seeking behavior",
keywords="health behavior",
keywords="consumer health information",
keywords="Hispanic Americans",
abstract="Background: Hispanics are the fastest-growing minority group in the United States and they suffer from a disproportionate burden of chronic diseases. Studies have shown that online health information has the potential to affect health behaviors and influence management of chronic disease for a significant proportion of the population, but little research has focused on Hispanics. Objective: The specific aim of this descriptive, cross-sectional study was to examine the association between online health information--seeking behaviors and health behaviors (physical activity, fruit and vegetable consumption, alcohol use, and hypertension medication adherence) among Hispanics. Methods: Data were collected from a convenience sample (N=2680) of Hispanics living in northern Manhattan by bilingual community health workers in a face-to-face interview and analyzed using linear and ordinal logistic regression. Variable selection and statistical analyses were guided by the Integrative Model of eHealth Use. Results: Only 7.38\% (198/2680) of the sample reported online health information--seeking behaviors. Levels of moderate physical activity and fruit, vegetable, and alcohol consumption were low. Among individuals taking hypertension medication (n=825), adherence was reported as high by approximately one-third (30.9\%, 255/825) of the sample. Controlling for demographic, situational, and literacy variables, online health information--seeking behaviors were significantly associated with fruit ($\beta$=0.35, 95\% CI 0.08-0.62, P=.01) and vegetable ($\beta$=0.36, 95\% CI 0.06-0.65, P=.02) consumption and physical activity ($\beta$=3.73, 95\% CI 1.99-5.46, P<.001), but not alcohol consumption or hypertension medication adherence. In the regression models, literacy factors, which were used as control variables, were associated with 3 health behaviors: social networking site membership (used to measure one dimension of computer literacy) was associated with fruit consumption ($\beta$=0.23, 95\% CI 0.05-0.42, P=.02), health literacy was associated with alcohol consumption ($\beta$=0.44, 95\% CI 0.24-0.63, P<.001), and hypertension medication adherence ($\beta$=--0.32, 95\% CI --0.62 to --0.03, P=.03). Models explained only a small amount of the variance in health behaviors. Conclusions: Given the promising, although modest, associations between online health information--seeking behaviors and some health behaviors, efforts are needed to improve Hispanics' ability to access and understand health information and to enhance the availability of online health information that is suitable in terms of language, readability level, and cultural relevance. ",
doi="10.2196/jmir.4368",
url="http://www.jmir.org/2015/11/e261/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26611438"
}


@Article{info:doi/10.2196/jmir.3650,
author="Antoniades, Athos
and Nicolaidou, Iolie
and Spachos, Dimitris
and Myll{\"a}ri, Jarkko
and Giordano, Daniela
and Dafli, Eleni
and Mitsopoulou, Evangelia
and Schizas, N. Christos
and Pattichis, Constantinos
and Nikolaidou, Maria
and Bamidis, Panagiotis",
title="Medical Content Searching, Retrieving, and Sharing Over the Internet: Lessons Learned From the mEducator Through a Scenario-Based Evaluation",
journal="J Med Internet Res",
year="2015",
month="Oct",
day="09",
volume="17",
number="10",
pages="e229",
keywords="searching and sharing of medical educational content",
keywords="repurposing",
keywords="metadata",
keywords="evaluation",
abstract="Background: The mEducator Best Practice Network (BPN) implemented and extended standards and reference models in e-learning to develop innovative frameworks as well as solutions that enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, and re-purposed across European Institutions, targeting medical students, doctors, educators and health care professionals. Scenario-based evaluation for usability testing, complemented with data from online questionnaires and field notes of users' performance, was designed and utilized for the evaluation of these solutions. Objective: The objective of this work is twofold: (1) to describe one instantiation of the mEducator BPN solutions (mEducator3.0 - ``MEdical Education LINnked Arena'' MELINA+) with a focus on the metadata schema used, as well as on other aspects of the system that pertain to usability and acceptance, and (2) to present evaluation results on the suitability of the proposed metadata schema for searching, retrieving, and sharing of medical content and with respect to the overall usability and acceptance of the system from the target users. Methods: A comprehensive evaluation methodology framework was developed and applied to four case studies, which were conducted in four different countries (ie, Greece, Cyprus, Bulgaria and Romania), with a total of 126 participants. In these case studies, scenarios referring to creating, sharing, and retrieving medical educational content using mEducator3.0 were used. The data were collected through two online questionnaires, consisting of 36 closed-ended questions and two open-ended questions that referred to mEducator 3.0 and through the use of field notes during scenario-based evaluations. Results: The main findings of the study showed that even though the informational needs of the mEducator target groups were addressed to a satisfactory extent and the metadata schema supported content creation, sharing, and retrieval from an end-user perspective, users faced difficulties in achieving a shared understanding of the meaning of some metadata fields and in correctly managing the intellectual property rights of repurposed content. Conclusions: The results of this evaluation impact researchers, medical professionals, and designers interested in using similar systems for educational content sharing in medical and other domains. Recommendations on how to improve the search, retrieval, identification, and obtaining of medical resources are provided, by addressing issues of content description metadata, content description procedures, and intellectual property rights for re-purposed content. ",
doi="10.2196/jmir.3650",
url="http://www.jmir.org/2015/10/e229/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26453250"
}


@Article{info:doi/10.2196/medinform.3982,
author="Ji, Xiaonan
and Yen, Po-Yin",
title="Using MEDLINE Elemental Similarity to Assist in the Article Screening Process for Systematic Reviews",
journal="JMIR Med Inform",
year="2015",
month="Aug",
day="31",
volume="3",
number="3",
pages="e28",
keywords="systematic review",
keywords="evidence-based medicine",
keywords="automatic document classification",
keywords="relevance feedback",
abstract="Background: Systematic reviews and their implementation in practice provide high quality evidence for clinical practice but are both time and labor intensive due to the large number of articles. Automatic text classification has proven to be instrumental in identifying relevant articles for systematic reviews. Existing approaches use machine learning model training to generate classification algorithms for the article screening process but have limitations. Objective: We applied a network approach to assist in the article screening process for systematic reviews using predetermined article relationships (similarity). The article similarity metric is calculated using the MEDLINE elements title (TI), abstract (AB), medical subject heading (MH), author (AU), and publication type (PT). We used an article network to illustrate the concept of article relationships. Using the concept, each article can be modeled as a node in the network and the relationship between 2 articles is modeled as an edge connecting them. The purpose of our study was to use the article relationship to facilitate an interactive article recommendation process. Methods: We used 15 completed systematic reviews produced by the Drug Effectiveness Review Project and demonstrated the use of article networks to assist article recommendation. We evaluated the predictive performance of MEDLINE elements and compared our approach with existing machine learning model training approaches. The performance was measured by work saved over sampling at 95\% recall (WSS95) and the F-measure (F1). We also used repeated analysis over variance and Hommel's multiple comparison adjustment to demonstrate statistical evidence. Results: We found that although there is no significant difference across elements (except AU), TI and AB have better predictive capability in general. Collaborative elements bring performance improvement in both F1 and WSS95. With our approach, a simple combination of TI+AB+PT could achieve a WSS95 performance of 37\%, which is competitive to traditional machine learning model training approaches (23\%-41\% WSS95). Conclusions: We demonstrated a new approach to assist in labor intensive systematic reviews. Predictive ability of different elements (both single and composited) was explored. Without using model training approaches, we established a generalizable method that can achieve a competitive performance. ",
doi="10.2196/medinform.3982",
url="http://medinform.jmir.org/2015/3/e28/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26323593"
}


@Article{info:doi/10.2196/jmir.4427,
author="Callahan, Alison
and Pernek, Igor
and Stiglic, Gregor
and Leskovec, Jure
and Strasberg, R. Howard
and Shah, Haresh Nigam",
title="Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance",
journal="J Med Internet Res",
year="2015",
month="Aug",
day="20",
volume="17",
number="8",
pages="e204",
keywords="Internet log analysis",
keywords="data mining",
keywords="physicians",
keywords="information-seeking behavior",
keywords="drug safety surveillance",
abstract="Background: Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance. Objective: To analyze health care professionals' information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource. Methods: Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert. Results: Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert. Conclusions: Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate. ",
doi="10.2196/jmir.4427",
url="http://www.jmir.org/2015/8/e204/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26293444"
}


@Article{info:doi/10.2196/jmir.4312,
author="Jamal, Amr
and Khan, A. Samina
and AlHumud, Ahmed
and Al-Duhyyim, Abdulaziz
and Alrashed, Mohammed
and Bin Shabr, Faisal
and Alteraif, Alwalid
and Almuziri, Abdullah
and Househ, Mowafa
and Qureshi, Riaz",
title="Association of Online Health Information--Seeking Behavior and Self-Care Activities Among Type 2 Diabetic Patients in Saudi Arabia",
journal="J Med Internet Res",
year="2015",
month="Aug",
day="12",
volume="17",
number="8",
pages="e196",
keywords="Internet",
keywords="diabetes mellitus, type 2",
keywords="self-care",
keywords="consumer health information",
keywords="telemedicine",
keywords="medical informatics",
keywords="health education",
keywords="Google",
keywords="eHealth",
keywords="e-patients",
keywords="health behavior",
keywords="Middle East",
keywords="Saudi Arabia",
abstract="Background: Health information obtained from the Internet has an impact on patient health care outcomes. There is a growing concern over the quality of online health information sources used by diabetic patients because little is known about their health information--seeking behavior and the impact this behavior has on their diabetes-related self-care, in particular in the Middle East setting. Objective: The aim of this study was to determine the online health-related information--seeking behavior among adult type 2 diabetic patients in the Middle East and the impact of their online health-related information--seeking behavior on their self-care activities. Methods: A cross-sectional survey was conducted on 344 patients with type 2 diabetes attending inpatient and outpatient primary health care clinics at 2 teaching hospitals in Riyadh, Saudi Arabia. The main outcome measures included the ability of patients to access the Internet, their ability to use the Internet to search for health-related information, and their responses to Internet searches in relation to their self-care activities. Further analysis of differences based on age, gender, sociodemographic, and diabetes-related self-care activities among online health-related information seekers and nononline health-related information seekers was conducted. Results: Among the 344 patients, 74.1\% (255/344) were male with a mean age of 53.5 (SD 13.8) years. Only 39.0\% (134/344) were Internet users; 71.6\% (96/134) of them used the Internet for seeking health-related information. Most participants reported that their primary source of health-related information was their physician (216/344, 62.8\%) followed by television (155/344, 45.1\%), family (113/344, 32.8\%), newspapers (100/344, 29.1\%), and the Internet (96/344, 27.9\%). Primary topics participants searched for were therapeutic diet for diabetes (55/96, 57\%) and symptoms of diabetes (52/96, 54\%) followed by diabetes treatment (50/96, 52\%). Long history of diabetes, familial history of the disease, unemployment, and not seeking diabetes education were the most common barriers for online health-related information--seeking behavior. Younger age, female, marital status, higher education, higher income, and longer duration of Internet usage were associated with more online health-related information--seeking behaviors. Most (89/96, 93\%) online health-related information seekers reported positive change in their behaviors after seeking online health information. Overall odds ratio (OR 1.56, 95\% CI 0.63-3.28) for all self-care responses demonstrated that there was no statistically significant difference between those seeking health-related information online and non--health-related information seekers. However, health-related information seekers were better in testing their blood glucose regularly, taking proper action for hyperglycemia, and adopting nonpharmacological management. Conclusions: Physicians and television are still the primary sources of health-related information for adult diabetic patients in Saudi Arabia whether they seek health-related information online or not. This study demonstrates that participants seeking online health-related information are more conscious about their diabetes self-care compared to non--health-related information seekers in some aspects more than the others. ",
doi="10.2196/jmir.4312",
url="http://www.jmir.org/2015/8/e196/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26268425"
}


@Article{info:doi/10.2196/jmir.3945,
author="Perez, L. Susan
and Paterniti, A. Debora
and Wilson, Machelle
and Bell, A. Robert
and Chan, Shan Man
and Villareal, C. Chloe
and Nguyen, Huy Hien
and Kravitz, L. Richard",
title="Characterizing the Processes for Navigating Internet Health Information Using Real-Time Observations: A Mixed-Methods Approach",
journal="J Med Internet Res",
year="2015",
month="Jul",
day="20",
volume="17",
number="7",
pages="e173",
keywords="dual processing",
keywords="information seeking",
keywords="Internet search",
keywords="health information",
abstract="Background: Little is known about the processes people use to find health-related information on the Internet or the individual characteristics that shape selection of information-seeking approaches. Objective: Our aim was to describe the processes by which users navigate the Internet for information about a hypothetical acute illness and to identify individual characteristics predictive of their information-seeking strategies. Methods: Study participants were recruited from public settings and agencies. Interested individuals were screened for eligibility using an online questionnaire. Participants listened to one of two clinical scenarios---consistent with influenza or bacterial meningitis---and then conducted an Internet search. Screen-capture video software captured Internet search mouse clicks and keystrokes. Each step of the search was coded as hypothesis testing (etiology), evidence gathering (symptoms), or action/treatment seeking (behavior). The coded steps were used to form a step-by-step pattern of each participant's information-seeking process. A total of 78 Internet health information seekers ranging from 21-35 years of age and who experienced barriers to accessing health care services participated. Results: We identified 27 unique patterns of information seeking, which were grouped into four overarching classifications based on the number of steps taken during the search, whether a pattern consisted of developing a hypothesis and exploring symptoms before ending the search or searching an action/treatment, and whether a pattern ended with action/treatment seeking. Applying dual-processing theory, we categorized the four overarching pattern classifications as either System 1 (41\%, 32/78), unconscious, rapid, automatic, and high capacity processing; or System 2 (59\%, 46/78), conscious, slow, and deliberative processing. Using multivariate regression, we found that System 2 processing was associated with higher education and younger age. Conclusions: We identified and classified two approaches to processing Internet health information. System 2 processing, a methodical approach, most resembles the strategies for information processing that have been found in other studies to be associated with higher-quality decisions. We conclude that the quality of Internet health-information seeking could be improved through consumer education on methodical Internet navigation strategies and the incorporation of decision aids into health information websites. ",
doi="10.2196/jmir.3945",
url="http://www.jmir.org/2015/7/e173/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26194787"
}


@Article{info:doi/10.2196/medinform.3740,
author="Yoo, Illhoi
and Mosa, Mohammad Abu Saleh",
title="Analysis of PubMed User Sessions Using a Full-Day PubMed Query Log: A Comparison of Experienced and Nonexperienced PubMed Users",
journal="JMIR Med Inform",
year="2015",
month="Jul",
day="02",
volume="3",
number="3",
pages="e25",
keywords="PubMed",
keywords="MEDLINE",
keywords="information retrieval",
keywords="experienced users",
keywords="nonexperienced users",
keywords="PubMed query log",
abstract="Background: PubMed is the largest biomedical bibliographic information source on the Internet. PubMed has been considered one of the most important and reliable sources of up-to-date health care evidence. Previous studies examined the effects of domain expertise/knowledge on search performance using PubMed. However, very little is known about PubMed users' knowledge of information retrieval (IR) functions and their usage in query formulation. Objective: The purpose of this study was to shed light on how experienced/nonexperienced PubMed users perform their search queries by analyzing a full-day query log. Our hypotheses were that (1) experienced PubMed users who use system functions quickly retrieve relevant documents and (2) nonexperienced PubMed users who do not use them have longer search sessions than experienced users. Methods: To test these hypotheses, we analyzed PubMed query log data containing nearly 3 million queries. User sessions were divided into two categories: experienced and nonexperienced. We compared experienced and nonexperienced users per number of sessions, and experienced and nonexperienced user sessions per session length, with a focus on how fast they completed their sessions. Results: To test our hypotheses, we measured how successful information retrieval was (at retrieving relevant documents), represented as the decrease rates of experienced and nonexperienced users from a session length of 1 to 2, 3, 4, and 5. The decrease rate (from a session length of 1 to 2) of the experienced users was significantly larger than that of the nonexperienced groups. Conclusions: Experienced PubMed users retrieve relevant documents more quickly than nonexperienced PubMed users in terms of session length. ",
doi="10.2196/medinform.3740",
url="http://medinform.jmir.org/2015/3/e25/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26139516"
}


@Article{info:doi/10.2196/mededu.4267,
author="O'Carroll, Marie Aoife
and Westby, Patricia Erin
and Dooley, Joseph
and Gordon, E. Kevin",
title="Information-Seeking Behaviors of Medical Students: A Cross-Sectional Web-Based Survey",
journal="JMIR Medical Education",
year="2015",
month="Jun",
day="29",
volume="1",
number="1",
pages="e4",
keywords="information-seeking behavior",
keywords="information retrieval",
keywords="Internet",
keywords="medical education",
keywords="medical students",
abstract="Background: Medical students face an information-rich environment in which retrieval and appraisal strategies are increasingly important. Objective: To describe medical students' current pattern of health information resource use and characterize their experience of instruction on information search and appraisal. Methods: We conducted a cross-sectional web-based survey of students registered in the four-year MD Program at Dalhousie University (Halifax, Nova Scotia, and Saint John, New Brunswick, sites), Canada. We collected self-reported data on information-seeking behavior, instruction, and evaluation of resources in the context of their medical education. Data were analyzed using descriptive statistics. Results: Surveys were returned by 213 of 462 eligible students (46.1\%). Most respondents (165/204, 80.9\%) recalled receiving formal instruction regarding information searches, but this seldom included nontraditional tools such as Google (23/107, 11.1\%), Wikipedia, or social media. In their daily practice, however, they reported heavy use of these tools, as well as EBM summaries. Accessibility, understandability, and overall usefulness were common features of highly used resources. Students identified challenges managing information and/or resource overload and source accessibility. Conclusions: Medical students receive instruction primarily on searching and assessing primary medical literature. In their daily practice, however, they rely heavily on nontraditional tools as well as EBM summaries. Attention to appropriate use and appraisal of nontraditional sources might enhance the current EBM curriculum. ",
doi="10.2196/mededu.4267",
url="http://mededu.jmir.org/2015/1/e4/",
url="http://www.ncbi.nlm.nih.gov/pubmed/27731842"
}


@Article{info:doi/10.2196/jmir.4305,
author="Yin, Zhijun
and Fabbri, Daniel
and Rosenbloom, Trent S.
and Malin, Bradley",
title="A Scalable Framework to Detect Personal Health Mentions on Twitter",
journal="J Med Internet Res",
year="2015",
month="Jun",
day="05",
volume="17",
number="6",
pages="e138",
keywords="consumer health",
keywords="information retrieval",
keywords="machine learning",
keywords="social media",
keywords="twitter",
keywords="infodemiology",
abstract="Background: Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health. Objective: The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed. Methods: We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status. Results: Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50\% of the time for 11 out of 34 (33\%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80\% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10\% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69\% (69/100) of the insomnia tweets disclosed the author's status, while only 1\% (1/100) disclosed another person's status. By contrast, 1\% (1/100) of the Down syndrome tweets disclosed the author's status, while 21\% (21/100) disclosed another person's status. Conclusions: It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes. ",
doi="10.2196/jmir.4305",
url="http://www.jmir.org/2015/6/e138/",
url="http://www.ncbi.nlm.nih.gov/pubmed/26048075"
}


@Article{info:doi/10.2196/resprot.3615,
author="Blanc, Xavier
and Collet, Tinh-Hai
and Auer, Reto
and Iriarte, Pablo
and Krause, Jan
and L{\'e}gar{\'e}, France
and Cornuz, Jacques
and Clair, Carole",
title="Retrieval of Publications Addressing Shared Decision Making: An Evaluation of Full-Text Searches on Medical Journal Websites",
journal="JMIR Res Protoc",
year="2015",
month="Apr",
day="07",
volume="4",
number="2",
pages="e38",
keywords="information storage and retrieval",
keywords="systematic reviews",
keywords="PubMed",
keywords="text mining",
keywords="full-text search",
keywords="decision making",
keywords="shared decision making",
abstract="Background: Full-text searches of articles increase the recall, defined by the proportion of relevant publications that are retrieved. However, this method is rarely used in medical research due to resource constraints. For the purpose of a systematic review of publications addressing shared decision making, a full-text search method was required to retrieve publications where shared decision making does not appear in the title or abstract. Objective: The objective of our study was to assess the efficiency and reliability of full-text searches in major medical journals for identifying shared decision making publications. Methods: A full-text search was performed on the websites of 15 high-impact journals in general internal medicine to look up publications of any type from 1996-2011 containing the phrase ``shared decision making''. The search method was compared with a PubMed search of titles and abstracts only. The full-text search was further validated by requesting all publications from the same time period from the individual journal publishers and searching through the collected dataset. Results: The full-text search for ``shared decision making'' on journal websites identified 1286 publications in 15 journals compared to 119 through the PubMed search. The search within the publisher-provided publications of 6 journals identified 613 publications compared to 646 with the full-text search on the respective journal websites. The concordance rate was 94.3\% between both full-text searches. Conclusions: Full-text searching on medical journal websites is an efficient and reliable way to identify relevant articles in the field of shared decision making for review or other purposes. It may be more widely used in biomedical research in other fields in the future, with the collaboration of publishers and journals toward open-access data. ",
doi="10.2196/resprot.3615",
url="http://www.researchprotocols.org/2015/2/e38/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25854180"
}


@Article{info:doi/10.2196/jmir.3326,
author="Li, Fan
and Li, Min
and Guan, Peng
and Ma, Shuang
and Cui, Lei",
title="Mapping Publication Trends and Identifying Hot Spots of Research on Internet Health Information Seeking Behavior: A Quantitative and Co-Word Biclustering Analysis",
journal="J Med Internet Res",
year="2015",
month="Mar",
day="25",
volume="17",
number="3",
pages="e81",
keywords="information seeking behavior",
keywords="Internet",
keywords="health information",
keywords="bibliometric analysis",
keywords="co-word analysis",
keywords="biclustering",
keywords="hot spots",
keywords="publication status",
abstract="Background: The Internet has become an established source of health information for people seeking health information. In recent years, research on the health information seeking behavior of Internet users has become an increasingly important scholarly focus. However, there have been no long-term bibliometric studies to date on Internet health information seeking behavior. Objective: The purpose of this study was to map publication trends and explore research hot spots of Internet health information seeking behavior. Methods: A bibliometric analysis based on PubMed was conducted to investigate the publication trends of research on Internet health information seeking behavior. For the included publications, the annual publication number, the distribution of countries, authors, languages, journals, and annual distribution of highly frequent major MeSH (Medical Subject Headings) terms were determined. Furthermore, co-word biclustering analysis of highly frequent major MeSH terms was utilized to detect the hot spots in this field. Results: A total of 533 publications were included. The research output was gradually increasing. There were five authors who published four or more articles individually. A total of 271 included publications (50.8\%) were written by authors from the United States, and 516 of the 533 articles (96.8\%) were published in English. The eight most active journals published 34.1\% (182/533) of the publications on this topic. Ten research hot spots were found: (1) behavior of Internet health information seeking about HIV infection or sexually transmitted diseases, (2) Internet health information seeking behavior of students, (3) behavior of Internet health information seeking via mobile phone and its apps, (4) physicians' utilization of Internet medical resources, (5) utilization of social media by parents, (6) Internet health information seeking behavior of patients with cancer (mainly breast cancer), (7) trust in or satisfaction with Web-based health information by consumers, (8) interaction between Internet utilization and physician-patient communication or relationship, (9) preference and computer literacy of people using search engines or other Web-based systems, and (10) attitude of people (especially adolescents) when seeking health information via the Internet. Conclusions: The 10 major research hot spots could provide some hints for researchers when launching new projects. The output of research on Internet health information seeking behavior is gradually increasing. Compared to the United States, the relatively small number of publications indexed by PubMed from other developed and developing countries indicates to some extent that the field might be still underdeveloped in many countries. More studies on Internet health information seeking behavior could give some references for health information providers. ",
doi="10.2196/jmir.3326",
url="http://www.jmir.org/2015/3/e81/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25830358"
}


@Article{info:doi/10.2196/jmir.3352,
author="Agree, M. Emily
and King, C. Abby
and Castro, M. Cynthia
and Wiley, Adrienne
and Borzekowski, LG Dina",
title="``It's Got to Be on This Page'': Age and Cognitive Style in a Study of Online Health Information Seeking",
journal="J Med Internet Res",
year="2015",
month="Mar",
day="24",
volume="17",
number="3",
pages="e79",
keywords="eHealth",
keywords="Internet",
keywords="health literacy",
keywords="age groups",
keywords="field dependence-independence",
abstract="Background: The extensive availability of online health information offers the public opportunities to become independently informed about their care, but what affects the successful retrieval and understanding of accurate and detailed information? We have limited knowledge about the ways individuals use the Internet and the personal characteristics that affect online health literacy. Objective: This study examined the extent to which age and cognitive style predicted success in searching for online health information, controlling for differences in education, daily Internet use, and general health literacy. Methods: The Online Health Study (OHS) was conducted at Johns Hopkins School of Public Health and Stanford University School of Medicine from April 2009 to June 2010. The OHS was designed to explore the factors associated with success in obtaining health information across different age groups. A total of 346 men and women aged 35 years and older of diverse racial and ethnic backgrounds participated in the study. Participants were evaluated for success in searching online for answers to health-related tasks/questions on nutrition, cancer, alternative medicine, vaccinations, medical equipment, and genetic testing. Results: Cognitive style, in terms of context sensitivity, was associated with less success in obtaining online health information, with tasks involving visual judgment most affected. In addition, better health literacy was positively associated with overall success in online health seeking, specifically for tasks requiring prior health knowledge. The oldest searchers were disadvantaged even after controlling for education, Internet use, general health literacy, and cognitive style, especially when spatial tasks such as mapping were involved. Conclusions: The increasing availability of online health information provides opportunities to improve patient education and knowledge, but effective use of these resources depends on online health literacy. Greater support for those who are in the oldest cohorts and for design of interfaces that support users with different cognitive styles may be required in an age of shared medical decision making. ",
doi="10.2196/jmir.3352",
url="http://www.jmir.org/2015/3/e79/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25831483"
}


@Article{info:doi/10.2196/medinform.3803,
author="Puspitasari, Ira
and Moriyama, Koichi
and Fukui, Ken--ichi
and Numao, Masayuki",
title="Effects of Individual Health Topic Familiarity on Activity Patterns During Health Information Searches",
journal="JMIR Med Inform",
year="2015",
month="Mar",
day="17",
volume="3",
number="1",
pages="e16",
keywords="health information search",
keywords="health search activity pattern",
keywords="health topic familiarity",
keywords="sequence of search activities",
abstract="Background: Non-medical professionals (consumers) are increasingly using the Internet to support their health information needs. However, the cognitive effort required to perform health information searches is affected by the consumer's familiarity with health topics. Consumers may have different levels of familiarity with individual health topics. This variation in familiarity may cause misunderstandings because the information presented by search engines may not be understood correctly by the consumers. Objective: As a first step toward the improvement of the health information search process, we aimed to examine the effects of health topic familiarity on health information search behaviors by identifying the common search activity patterns exhibited by groups of consumers with different levels of familiarity. Methods: Each participant completed a health terminology familiarity questionnaire and health information search tasks. The responses to the familiarity questionnaire were used to grade the familiarity of participants with predefined health topics. The search task data were transcribed into a sequence of search activities using a coding scheme. A computational model was constructed from the sequence data using a Markov chain model to identify the common search patterns in each familiarity group. Results: Forty participants were classified into L1 (not familiar), L2 (somewhat familiar), and L3 (familiar) groups based on their questionnaire responses. They had different levels of familiarity with four health topics. The video data obtained from all of the participants were transcribed into 4595 search activities (mean 28.7, SD 23.27 per session). The most frequent search activities and transitions in all the familiarity groups were related to evaluations of the relevancy of selected web pages in the retrieval results. However, the next most frequent transitions differed in each group and a chi-squared test confirmed this finding (P<.001). Next, according to the results of a perplexity evaluation, the health information search patterns were best represented as a 5-gram sequence pattern. The most common patterns in group L1 were frequent query modifications, with relatively low search efficiency, and accessing and evaluating selected results from a health website. Group L2 performed frequent query modifications, but with better search efficiency, and accessed and evaluated selected results from a health website. Finally, the members of group L3 successfully discovered relevant results from the first query submission, performed verification by accessing several health websites after they discovered relevant results, and directly accessed consumer health information websites. Conclusions: Familiarity with health topics affects health information search behaviors. Our analysis of state transitions in search activities detected unique behaviors and common search activity patterns in each familiarity group during health information searches. ",
doi="10.2196/medinform.3803",
url="http://medinform.jmir.org/2015/1/e16/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25783222"
}


@Article{info:doi/10.2196/jmir.3388,
author="Luo, Jake
and Zhang, Guo-Qiang
and Wentz, Susan
and Cui, Licong
and Xu, Rong",
title="SimQ: Real-Time Retrieval of Similar Consumer Health Questions",
journal="J Med Internet Res",
year="2015",
month="Feb",
day="17",
volume="17",
number="2",
pages="e43",
keywords="Online Health Information Seeking",
keywords="Health Information Delivery",
keywords="Consumer Health Informatics",
keywords="Consumer Question Retrieval",
keywords="Similarity Analysis",
keywords="Netwellness.org",
keywords="Health Care Questions",
keywords="Search and Query",
abstract="Background: There has been a significant increase in the popularity of Web-based question-and-answer (Q\&A) services that provide health care information for consumers. Large amounts of Q\&As have been archived in these online communities, which form a valuable knowledge base for consumers who seek answers to their health care concerns. However, due to consumers' possible lack of professional knowledge, it is still very challenging for them to find Q\&As that are closely relevant to their own health problems. Consumers often repeatedly ask similar questions that have already been answered previously by other users. Objective: In this study, we aim to develop efficient informatics methods that can retrieve similar Web-based consumer health questions using syntactic and semantic analysis. Methods: We propose the ``SimQ'' to achieve this objective. SimQ is an informatics framework that compares the similarity of archived health questions and retrieves answers to satisfy consumers' information needs. Statistical syntactic parsing was used to analyze each question's syntactic structure. Standardized Unified Medical Language System (UMLS) was employed to annotate semantic types and extract medical concepts. Finally, the similarity between sentences was calculated using both semantic and syntactic features. Results: We used 2000 randomly selected consumer questions to evaluate the system's performance. The results show that SimQ reached the highest precision of 72.2\%, recall of 78.0\%, and F-score of 75.0\% when using compositional feature representations. Conclusions: We demonstrated that SimQ complements the existing Q\&A services of Netwellness, a not-for-profit community-based consumer health information service that consists of nearly 70,000 Q\&As and serves over 3 million users each year. SimQ not only reduces response delay by instantly providing closely related questions and answers, but also helps consumers to improve the understanding of their health concerns. ",
doi="10.2196/jmir.3388",
url="http://www.jmir.org/2015/2/e43/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25689608"
}


@Article{info:doi/10.2196/jmir.3702,
author="Manuvinakurike, Ramesh
and Velicer, F. Wayne
and Bickmore, W. Timothy",
title="Automated Indexing of Internet Stories for Health Behavior Change: Weight Loss Attitude Pilot Study",
journal="J Med Internet Res",
year="2014",
month="Dec",
day="09",
volume="16",
number="12",
pages="e285",
keywords="behavioral medicine",
keywords="natural language processing",
keywords="animation",
keywords="consumer health",
keywords="health informatics",
keywords="self-efficacy",
abstract="Background: Automated health behavior change interventions show promise, but suffer from high attrition and disuse. The Internet abounds with thousands of personal narrative accounts of health behavior change that could not only provide useful information and motivation for others who are also trying to change, but an endless source of novel, entertaining stories that may keep participants more engaged than messages authored by interventionists. Objective: Given a collection of relevant personal health behavior change stories gathered from the Internet, the aim of this study was to develop and evaluate an automated indexing algorithm that could select the best possible story to provide to a user to have the greatest possible impact on their attitudes toward changing a targeted health behavior, in this case weight loss. Methods: An indexing algorithm was developed using features informed by theories from behavioral medicine together with text classification and machine learning techniques. The algorithm was trained using a crowdsourced dataset, then evaluated in a 2{\texttimes}2 between-subjects randomized pilot study. One factor compared the effects of participants reading 2 indexed stories vs 2 randomly selected stories, whereas the second factor compared the medium used to tell the stories: text or animated conversational agent. Outcome measures included changes in self-efficacy and decisional balance for weight loss before and after the stories were read. Results: Participants were recruited from a crowdsourcing website (N=103; 53.4\%, 55/103 female; mean age 35, SD 10.8 years; 65.0\%, 67/103 precontemplation; 19.4\%, 20/103 contemplation for weight loss). Participants who read indexed stories exhibited a significantly greater increase in self-efficacy for weight loss compared to the control group (F1,107=5.5, P=.02). There were no significant effects of indexing on change in decisional balance (F1,97=0.05, P=.83) and no significant effects of medium on change in self-efficacy (F1,107=0.04, P=.84) or decisional balance (F1,97=0.78, P=.38). Conclusions: Personal stories of health behavior change can be harvested from the Internet and used directly and automatically in interventions to affect participant attitudes, such as self-efficacy for changing behavior. Such approaches have the potential to provide highly tailored interventions that maximize engagement and retention with minimal intervention development effort. ",
doi="10.2196/jmir.3702",
url="http://www.jmir.org/2014/12/e285/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25491243"
}


@Article{info:doi/10.2196/jmir.3706,
author="Lee, Kenneth
and Hoti, Kreshnik
and Hughes, David Jeffery
and Emmerton, Lynne",
title="Dr Google and the Consumer: A Qualitative Study Exploring the Navigational Needs and Online Health Information-Seeking Behaviors of Consumers With Chronic Health Conditions",
journal="J Med Internet Res",
year="2014",
month="Dec",
day="02",
volume="16",
number="12",
pages="e262",
keywords="online health information seeking",
keywords="health information search",
keywords="health seeking behavior",
keywords="consumer health information",
keywords="information needs",
keywords="Internet",
keywords="chronic disease",
keywords="patients",
keywords="qualitative research",
keywords="interview",
abstract="Background: The abundance of health information available online provides consumers with greater access to information pertinent to the management of health conditions. This is particularly important given an increasing drive for consumer-focused health care models globally, especially in the management of chronic health conditions, and in recognition of challenges faced by lay consumers with finding, understanding, and acting on health information sourced online. There is a paucity of literature exploring the navigational needs of consumers with regards to accessing online health information. Further, existing interventions appear to be didactic in nature, and it is unclear whether such interventions appeal to consumers' needs. Objective: Our goal was to explore the navigational needs of consumers with chronic health conditions in finding online health information within the broader context of consumers' online health information-seeking behaviors. Potential barriers to online navigation were also identified. Methods: Semistructured interviews were conducted with adult consumers who reported using the Internet for health information and had at least one chronic health condition. Participants were recruited from nine metropolitan community pharmacies within Western Australia, as well as through various media channels. Interviews were audio-recorded, transcribed verbatim, and then imported into QSR NVivo 10. Two established approaches to thematic analysis were adopted. First, a data-driven approach was used to minimize potential bias in analysis and improve construct and criterion validity. A theory-driven approach was subsequently used to confirm themes identified by the former approach and to ensure identified themes were relevant to the objectives. Two levels of analysis were conducted for both data-driven and theory-driven approaches: manifest-level analysis, whereby face-value themes were identified, and latent-level analysis, whereby underlying concepts were identified. Results: We conducted 17 interviews, with data saturation achieved by the 14th interview. While we identified a broad range of online health information-seeking behaviors, most related to information discussed during consumer-health professional consultations such as looking for information about medication side effects. The barriers we identified included intrinsic barriers, such as limited eHealth literacy, and extrinsic barriers, such as the inconsistency of information between different online sources. The navigational needs of our participants were extrinsic in nature and included health professionals directing consumers to appropriate online resources and better filtering of online health information. Our participants' online health information-seeking behaviors, reported barriers, and navigational needs were underpinned by the themes of trust, patient activation, and relevance. Conclusions: This study suggests that existing interventions aimed to assist consumers with navigating online health information may not be what consumers want or perceive they need. eHealth literacy and patient activation appear to be prevalent concepts in the context of consumers' online health information-seeking behaviors. Furthermore, the role for health professionals in guiding consumers to quality online health information is highlighted. ",
doi="10.2196/jmir.3706",
url="http://www.jmir.org/2014/12/e262/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25470306"
}


@Article{info:doi/10.2196/jmir.3836,
author="Griffon, Nicolas
and Schuers, Matthieu
and Soualmia, Fatima Lina
and Grosjean, Julien
and Kerdelhu{\'e}, Ga{\'e}tan
and Kergourlay, Ivan
and Dahamna, Badisse
and Darmoni, Jacques St{\'e}fan",
title="A Search Engine to Access PubMed Monolingual Subsets: Proof of Concept and Evaluation in French",
journal="J Med Internet Res",
year="2014",
month="Dec",
day="01",
volume="16",
number="12",
pages="e271",
keywords="databases, bibliographic",
keywords="French language",
keywords="information storage and retrieval",
keywords="PubMed",
keywords="user-computer interface",
keywords="search engine",
abstract="Background: PubMed contains numerous articles in languages other than English. However, existing solutions to access these articles in the language in which they were written remain unconvincing. Objective: The aim of this study was to propose a practical search engine, called Multilingual PubMed, which will permit access to a PubMed subset in 1 language and to evaluate the precision and coverage for the French version (Multilingual PubMed-French). Methods: To create this tool, translations of MeSH were enriched (eg, adding synonyms and translations in French) and integrated into a terminology portal. PubMed subsets in several European languages were also added to our database using a dedicated parser. The response time for the generic semantic search engine was evaluated for simple queries. BabelMeSH, Multilingual PubMed-French, and 3 different PubMed strategies were compared by searching for literature in French. Precision and coverage were measured for 20 randomly selected queries. The results were evaluated as relevant to title and abstract, the evaluator being blind to search strategy. Results: More than 650,000 PubMed citations in French were integrated into the Multilingual PubMed-French information system. The response times were all below the threshold defined for usability (2 seconds). Two search strategies (Multilingual PubMed-French and 1 PubMed strategy) showed high precision (0.93 and 0.97, respectively), but coverage was 4 times higher for Multilingual PubMed-French. Conclusions: It is now possible to freely access biomedical literature using a practical search tool in French. This tool will be of particular interest for health professionals and other end users who do not read or query sufficiently in English. The information system is theoretically well suited to expand the approach to other European languages, such as German, Spanish, Norwegian, and Portuguese. ",
doi="10.2196/jmir.3836",
url="http://www.jmir.org/2014/12/e271/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25448528"
}


@Article{info:doi/10.2196/jmir.3341,
author="Fiksdal, S. Alexander
and Kumbamu, Ashok
and Jadhav, S. Ashutosh
and Cocos, Cristian
and Nelsen, A. Laurie
and Pathak, Jyotishman
and McCormick, B. Jennifer",
title="Evaluating the Process of Online Health Information Searching: A Qualitative Approach to Exploring Consumer Perspectives",
journal="J Med Internet Res",
year="2014",
month="Oct",
day="07",
volume="16",
number="10",
pages="e224",
keywords="Internet",
keywords="information seeking behavior",
keywords="consumer health information",
keywords="qualitative research",
abstract="Background: The Internet is a common resource that patients and consumers use to access health-related information. Multiple practical, cultural, and socioeconomic factors influence why, when, and how people utilize this tool. Improving the delivery of health-related information necessitates a thorough understanding of users' searching-related needs, preferences, and experiences. Although a wide body of quantitative research examining search behavior exists, qualitative approaches have been under-utilized and provide unique perspectives that may prove useful in improving the delivery of health information over the Internet. Objective: We conducted this study to gain a deeper understanding of online health-searching behavior in order to inform future developments of personalizing information searching and content delivery. Methods: We completed three focus groups with adult residents of Olmsted County, Minnesota, which explored perceptions of online health information searching. Participants were recruited through flyers and classifieds advertisements posted throughout the community. We audio-recorded and transcribed all focus groups, and analyzed data using standard qualitative methods. Results: Almost all participants reported using the Internet to gather health information. They described a common experience of searching, filtering, and comparing results in order to obtain information relevant to their intended search target. Information saturation and fatigue were cited as main reasons for terminating searching. This information was often used as a resource to enhance their interactions with health care providers. Conclusions: Many participants viewed the Internet as a valuable tool for finding health information in order to support their existing health care resources. Although the Internet is a preferred source of health information, challenges persist in streamlining the search process. Content providers should continue to develop new strategies and technologies aimed at accommodating diverse populations, vocabularies, and health information needs. ",
doi="10.2196/jmir.3341",
url="http://www.jmir.org/2014/10/e224/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25348028"
}


@Article{info:doi/10.2196/jmir.3369,
author="Choong, Keen Miew
and Galgani, Filippo
and Dunn, G. Adam
and Tsafnat, Guy",
title="Automatic Evidence Retrieval for Systematic Reviews",
journal="J Med Internet Res",
year="2014",
month="Oct",
day="01",
volume="16",
number="10",
pages="e223",
keywords="evidence-based medicine",
keywords="medical informatics",
keywords="information storage and retrieval",
abstract="Background: Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing's effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious. Objective: Our goal was to evaluate an automatic method for citation snowballing's capacity to identify and retrieve the full text and/or abstracts of cited articles. Methods: Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0\% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F1 score. Results: The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7\%, F1 score=79.3\%; as proportion of citations in MAS: recall=85.5\%, F1 score=91.2\%) of citations with high precision (97.7\%), and retrieved the full text or abstract for 490 (recall=82.9\%, precision=92.1\%, F1 score=87.3\%) of the 633 correctly retrieved citations. Conclusions: The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews. ",
doi="10.2196/jmir.3369",
url="http://www.jmir.org/2014/10/e223/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25274020"
}


@Article{info:doi/10.2196/jmir.3186,
author="Jadhav, Ashutosh
and Andrews, Donna
and Fiksdal, Alexander
and Kumbamu, Ashok
and McCormick, B. Jennifer
and Misitano, Andrew
and Nelsen, Laurie
and Ryu, Euijung
and Sheth, Amit
and Wu, Stephen
and Pathak, Jyotishman",
title="Comparative Analysis of Online Health Queries Originating From Personal Computers and Smart Devices on a Consumer Health Information Portal",
journal="J Med Internet Res",
year="2014",
month="Jul",
day="04",
volume="16",
number="7",
pages="e160",
keywords="online health information seeking",
keywords="health information search",
keywords="eHealth",
keywords="mHealth",
keywords="search query analysis",
keywords="health search log",
keywords="mobile health",
keywords="health seeking behavior",
abstract="Background: The number of people using the Internet and mobile/smart devices for health information seeking is increasing rapidly. Although the user experience for online health information seeking varies with the device used, for example, smart devices (SDs) like smartphones/tablets versus personal computers (PCs) like desktops/laptops, very few studies have investigated how online health information seeking behavior (OHISB) may differ by device. Objective: The objective of this study is to examine differences in OHISB between PCs and SDs through a comparative analysis of large-scale health search queries submitted through Web search engines from both types of devices. Methods: Using the Web analytics tool, IBM NetInsight OnDemand, and based on the type of devices used (PCs or SDs), we obtained the most frequent health search queries between June 2011 and May 2013 that were submitted on Web search engines and directed users to the Mayo Clinic's consumer health information website. We performed analyses on ``Queries with considering repetition counts (QwR)'' and ``Queries without considering repetition counts (QwoR)''. The dataset contains (1) 2.74 million and 3.94 million QwoR, respectively for PCs and SDs, and (2) more than 100 million QwR for both PCs and SDs. We analyzed structural properties of the queries (length of the search queries, usage of query operators and special characters in health queries), types of search queries (keyword-based, wh-questions, yes/no questions), categorization of the queries based on health categories and information mentioned in the queries (gender, age-groups, temporal references), misspellings in the health queries, and the linguistic structure of the health queries. Results: Query strings used for health information searching via PCs and SDs differ by almost 50\%. The most searched health categories are ``Symptoms'' (1 in 3 search queries), ``Causes'', and ``Treatments \& Drugs''. The distribution of search queries for different health categories differs with the device used for the search. Health queries tend to be longer and more specific than general search queries. Health queries from SDs are longer and have slightly fewer spelling mistakes than those from PCs. Users specify words related to women and children more often than that of men and any other age group. Most of the health queries are formulated using keywords; the second-most common are wh- and yes/no questions. Users ask more health questions using SDs than PCs. Almost all health queries have at least one noun and health queries from SDs are more descriptive than those from PCs. Conclusions: This study is a large-scale comparative analysis of health search queries to understand the effects of device type (PCs vs SDs) used on OHISB. The study indicates that the device used for online health information search plays an important role in shaping how health information searches by consumers and patients are executed. ",
doi="10.2196/jmir.3186",
url="http://www.jmir.org/2014/7/e160/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25000537"
}


@Article{info:doi/10.2196/medinform.3037,
author="Petersen, Henry
and Poon, Josiah
and Poon, K. Simon
and Loy, Clement",
title="Increased Workload for Systematic Review Literature Searches of Diagnostic Tests Compared With Treatments: Challenges and Opportunities",
journal="JMIR Med Inform",
year="2014",
month="May",
day="27",
volume="2",
number="1",
pages="e11",
keywords="meta-analysis",
keywords="data mining",
keywords="review literature",
keywords="information storage and retrieval",
keywords="classification and clustering",
abstract="Background: Comprehensive literature searches are conducted over multiple medical databases in order to meet stringent quality standards for systematic reviews. These searches are often very laborious, with authors often manually screening thousands of articles. Information retrieval (IR) techniques have proven increasingly effective in improving the efficiency of this process. IR challenges for systematic reviews involve building classifiers using training data with very high class-imbalance, and meeting the requirement for near perfect recall on relevant studies. Traditionally, most systematic reviews have focused on questions relating to treatment. The last decade has seen a large increase in the number of systematic reviews of diagnostic test accuracy (DTA). Objective: We aim to demonstrate that DTA reviews comprise an especially challenging subclass of systematic reviews with respect to the workload required for literature screening. We identify specific challenges for the application of IR to literature screening for DTA reviews, and identify potential directions for future research. Methods: We hypothesize that IR for DTA reviews face three additional challenges, compared to systematic reviews of treatments. These include an increased class-imbalance, a broader definition of the target class, and relative inadequacy of available metadata (ie, medical subject headings (MeSH) terms for medical literature analysis and retrieval system online). Assuming these hypotheses to be true, we identify five manifestations when we compare literature searches of DTA versus treatment. These manifestations include: an increase in the average number of articles screened, and increase in the average number of full-text articles obtained, a decrease in the number of included studies as a percentage of full-text articles screened, a decrease in the number of included studies as a percentage of all articles screened, and a decrease in the number of full-text articles obtained as a percentage of all articles screened. As of July 12 2013, 13 published Cochrane DTA reviews were available and all were included. For each DTA review, we randomly selected 15 treatment reviews published by the corresponding Cochrane Review Group (N=195). We then statistically tested differences in these five hypotheses, for the DTA versus treatment reviews. Results: Despite low statistical power caused by the small sample size for DTA reviews, strong (P<.01) or very strong (P<.001) evidence was obtained to support three of the five expected manifestations, with evidence for at least one manifestation of each hypothesis. The observed difference in effect sizes are substantial, demonstrating the practical difference in reviewer workload. Conclusions: Reviewer workload (volume of citations screened) when screening literature for systematic reviews of DTA is especially high. This corresponds to greater rates of class-imbalance when training classifiers for automating literature screening for DTA reviews. Addressing concerns such as lower quality metadata and effectively modelling the broader target class could help to alleviate such challenges, providing possible directions for future research. ",
doi="10.2196/medinform.3037",
url="http://medinform.jmir.org/2014/1/e11/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25600450"
}


@Article{info:doi/10.2196/jmir.2642,
author="Allam, Ahmed
and Schulz, Johannes Peter
and Nakamoto, Kent",
title="The Impact of Search Engine Selection and Sorting Criteria on Vaccination Beliefs and Attitudes: Two Experiments Manipulating Google Output",
journal="J Med Internet Res",
year="2014",
month="Apr",
day="02",
volume="16",
number="4",
pages="e100",
keywords="consumer health information",
keywords="search engine",
keywords="searching behavior",
keywords="Internet",
keywords="information storage and retrieval",
keywords="online systems",
keywords="public health informatics",
keywords="vaccination",
keywords="health communication",
abstract="Background: During the past 2 decades, the Internet has evolved to become a necessity in our daily lives. The selection and sorting algorithms of search engines exert tremendous influence over the global spread of information and other communication processes. Objective: This study is concerned with demonstrating the influence of selection and sorting/ranking criteria operating in search engines on users' knowledge, beliefs, and attitudes of websites about vaccination. In particular, it is to compare the effects of search engines that deliver websites emphasizing on the pro side of vaccination with those focusing on the con side and with normal Google as a control group. Method: We conducted 2 online experiments using manipulated search engines. A pilot study was to verify the existence of dangerous health literacy in connection with searching and using health information on the Internet by exploring the effect of 2 manipulated search engines that yielded either pro or con vaccination sites only, with a group receiving normal Google as control. A pre-post test design was used; participants were American marketing students enrolled in a study-abroad program in Lugano, Switzerland. The second experiment manipulated the search engine by applying different ratios of con versus pro vaccination webpages displayed in the search results. Participants were recruited from Amazon's Mechanical Turk platform where it was published as a human intelligence task (HIT). Results: Both experiments showed knowledge highest in the group offered only pro vaccination sites (Z=--2.088, P=.03; Kruskal-Wallis H test [H5]=11.30, P=.04). They acknowledged the importance/benefits (Z=--2.326, P=.02; H5=11.34, P=.04) and effectiveness (Z=--2.230, P=.03) of vaccination more, whereas groups offered antivaccination sites only showed increased concern about effects (Z=--2.582, P=.01; H5=16.88, P=.005) and harmful health outcomes (Z=--2.200, P=.02) of vaccination. Normal Google users perceived information quality to be positive despite a small effect on knowledge and a negative effect on their beliefs and attitudes toward vaccination and willingness to recommend the information ($\chi$25=14.1, P=.01). More exposure to antivaccination websites lowered participants' knowledge (J=4783.5, z=?2.142, P=.03) increased their fear of side effects (J=6496, z=2.724, P=.006), and lowered their acknowledgment of benefits (J=4805, z=--2.067, P=.03). Conclusion: The selection and sorting/ranking criteria of search engines play a vital role in online health information seeking. Search engines delivering websites containing credible and evidence-based medical information impact positively Internet users seeking health information. Whereas sites retrieved by biased search engines create some opinion change in users. These effects are apparently independent of users' site credibility and evaluation judgments. Users are affected beneficially or detrimentally but are unaware, suggesting they are not consciously perceptive of indicators that steer them toward the credible sources or away from the dangerous ones. In this sense, the online health information seeker is flying blind. ",
doi="10.2196/jmir.2642",
url="http://www.jmir.org/2014/4/e100/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24694866"
}


@Article{info:doi/10.2196/jmir.3044,
author="Schweiger, Stefan
and Oeberst, Aileen
and Cress, Ulrike",
title="Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation",
journal="J Med Internet Res",
year="2014",
month="Mar",
day="26",
volume="16",
number="3",
pages="e94",
keywords="Web-based systems",
keywords="prejudice",
keywords="folksonomy",
keywords="taxonomy",
keywords="collaborative tagging",
keywords="human information processing",
keywords="psychotherapy",
keywords="pharmacotherapy",
abstract="Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective. Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts' evaluations. Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts' blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants' treatment recommendation after information search. Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; t173=7.67, P<.001, d=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (F2,168=10.61, P<.001, partial eta squared=0.112), blog post selection (F2,168=6.55, P=.002, partial eta squared=0.072), and treatment efficacy ratings (F2,168=8.48, P<.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (F1,168=4.32, P=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (Ps>.33). Conclusions: We conclude that the psychotherapy bias is most effectively attenuated---and even eliminated---when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion. ",
doi="10.2196/jmir.3044",
url="http://www.jmir.org/2014/3/e94/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24670677"
}


@Article{info:doi/10.2196/medinform.3172,
author="Adamusiak, Tomasz
and Shimoyama, Naoki
and Shimoyama, Mary",
title="Next Generation Phenotyping Using the Unified Medical Language System",
journal="JMIR Med Inform",
year="2014",
month="Mar",
day="18",
volume="2",
number="1",
pages="e5",
keywords="meaningful use",
keywords="semantic interoperability",
keywords="UMLS",
keywords="SNOMED CT",
keywords="LOINC",
keywords="RxNorm",
keywords="CPT",
keywords="HCPCS",
keywords="ICD-9",
keywords="ICD-10",
abstract="Background: Structured information within patient medical records represents a largely untapped treasure trove of research data. In the United States, privacy issues notwithstanding, this has recently become more accessible thanks to the increasing adoption of electronic health records (EHR) and health care data standards fueled by the Meaningful Use legislation. The other side of the coin is that it is now becoming increasingly more difficult to navigate the profusion of many disparate clinical terminology standards, which often span millions of concepts. Objective: The objective of our study was to develop a methodology for integrating large amounts of structured clinical information that is both terminology agnostic and able to capture heterogeneous clinical phenotypes including problems, procedures, medications, and clinical results (such as laboratory tests and clinical observations). In this context, we define phenotyping as the extraction of all clinically relevant features contained in the EHR. Methods: The scope of the project was framed by the Common Meaningful Use (MU) Dataset terminology standards; the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), RxNorm, the Logical Observation Identifiers Names and Codes (LOINC), the Current Procedural Terminology (CPT), the Health care Common Procedure Coding System (HCPCS), the International Classification of Diseases Ninth Revision Clinical Modification (ICD-9-CM), and the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM). The Unified Medical Language System (UMLS) was used as a mapping layer among the MU ontologies. An extract, load, and transform approach separated original annotations in the EHR from the mapping process and allowed for continuous updates as the terminologies were updated. Additionally, we integrated all terminologies into a single UMLS derived ontology and further optimized it to make the relatively large concept graph manageable. Results: The initial evaluation was performed with simulated data from the Clinical Avatars project using 100,000 virtual patients undergoing a 90 day, genotype guided, warfarin dosing protocol. This dataset was annotated with standard MU terminologies, loaded, and transformed using the UMLS. We have deployed this methodology to scale in our in-house analytics platform using structured EHR data for 7931 patients (12 million clinical observations) treated at the Froedtert Hospital. A demonstration limited to Clinical Avatars data is available on the Internet using the credentials user ``jmirdemo'' and password ``jmirdemo''. Conclusions: Despite its inherent complexity, the UMLS can serve as an effective interface terminology for many of the clinical data standards currently used in the health care domain. ",
doi="10.2196/medinform.3172",
url="http://medinform.jmir.org/2014/1/e5/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25601137"
}


@Article{info:doi/10.2196/medinform.3205,
author="Harvey, Harlan
and Krishnaraj, Arun
and Alkasab, K. Tarik",
title="Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records",
journal="JMIR Med Inform",
year="2014",
month="Mar",
day="11",
volume="2",
number="1",
pages="e4",
keywords="medical informatics",
keywords="medical records systems",
keywords="computerized",
keywords="health information management",
doi="10.2196/medinform.3205",
url="http://medinform.jmir.org/2014/1/e4/",
url="http://www.ncbi.nlm.nih.gov/pubmed/25601018"
}


@Article{info:doi/10.2196/ijmr.2811,
author="Cook, A. David
and Enders, Felicity
and Linderbaum, A. Jane
and Zwart, Dale
and Lloyd, J. Farrell",
title="Speed and Accuracy of a Point of Care Web-Based Knowledge Resource for Clinicians: A Controlled Crossover Trial",
journal="Interact J Med Res",
year="2014",
month="Feb",
day="21",
volume="3",
number="1",
pages="e7",
keywords="medical education",
keywords="Web-based learning",
keywords="educational technology",
keywords="clinical decision support",
keywords="health information technology",
abstract="Background: Effective knowledge translation at the point of care requires that clinicians quickly find correct answers to clinical questions, and that they have appropriate confidence in their answers. Web-based knowledge resources can facilitate this process. Objective: The objective of our study was to evaluate a novel Web-based knowledge resource in comparison with other available Web-based resources, using outcomes of accuracy, time, and confidence. Methods: We conducted a controlled, crossover trial involving 59 practicing clinicians. Each participant answered questions related to two clinical scenarios. For one scenario, participants used a locally developed Web-based resource, and for the second scenario, they used other self-selected Web-based resources. The local knowledge resource (``AskMayoExpert'') was designed to provide very concise evidence-based answers to commonly asked clinical questions. Outcomes included time to a correct response with at least 80\% confidence (primary outcome), accuracy, time, and confidence. Results: Answers were more often accurate when using the local resource than when using other Web-based resources, with odds ratio 6.2 (95\% CI 2.6-14.5; P<.001) when averaged across scenarios. Time to find an answer was faster, and confidence in that answer was consistently higher, for the local resource (P<.001). Overconfidence was also less frequent with the local resource. In a time-to-event analysis, the chance of responding correctly with at least 80\% confidence was 2.5 times greater when using the local resource than with other resources (95\% CI 1.6-3.8; P<.001). Conclusions: Clinicians using a Web-based knowledge resource designed to provide quick, concise answers at the point of care found answers with greater accuracy and confidence than when using other self-selected Web-based resources. Further study to improve the design and implementation of knowledge resources may improve point of care learning. ",
doi="10.2196/ijmr.2811",
url="http://www.i-jmr.org/2014/1/e7/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24566739"
}


@Article{info:doi/10.2196/jmir.3111,
author="Cui, Licong
and Carter, Rebecca
and Zhang, Guo-Qiang",
title="Evaluation of a Novel Conjunctive Exploratory Navigation Interface for Consumer Health Information: A Crowdsourced Comparative Study",
journal="J Med Internet Res",
year="2014",
month="Feb",
day="10",
volume="16",
number="2",
pages="e45",
keywords="crowdsourcing",
keywords="consumer health information",
keywords="human computer interaction",
keywords="information retrieval",
keywords="search interfaces",
keywords="comparative user evaluation",
abstract="Background: Numerous consumer health information websites have been developed to provide consumers access to health information. However, lookup search is insufficient for consumers to take full advantage of these rich public information resources. Exploratory search is considered a promising complementary mechanism, but its efficacy has never before been rigorously evaluated for consumer health information retrieval interfaces. Objective: This study aims to (1) introduce a novel Conjunctive Exploratory Navigation Interface (CENI) for supporting effective consumer health information retrieval and navigation, and (2) evaluate the effectiveness of CENI through a search-interface comparative evaluation using crowdsourcing with Amazon Mechanical Turk (AMT). Methods: We collected over 60,000 consumer health questions from NetWellness, one of the first consumer health websites to provide high-quality health information. We designed and developed a novel conjunctive exploratory navigation interface to explore NetWellness health questions with health topics as dynamic and searchable menus. To investigate the effectiveness of CENI, we developed a second interface with keyword-based search only. A crowdsourcing comparative study was carefully designed to compare three search modes of interest: (A) the topic-navigation-based CENI, (B) the keyword-based lookup interface, and (C) either the most commonly available lookup search interface with Google, or the resident advanced search offered by NetWellness. To compare the effectiveness of the three search modes, 9 search tasks were designed with relevant health questions from NetWellness. Each task included a rating of difficulty level and questions for validating the quality of answers. Ninety anonymous and unique AMT workers were recruited as participants. Results: Repeated-measures ANOVA analysis of the data showed the search modes A, B, and C had statistically significant differences among their levels of difficulty (P<.001). Wilcoxon signed-rank test (one-tailed) between A and B showed that A was significantly easier than B (P<.001). Paired t tests (one-tailed) between A and C showed A was significantly easier than C (P<.001). Participant responses on the preferred search modes showed that 47.8\% (43/90) participants preferred A, 25.6\% (23/90) preferred B, 24.4\% (22/90) preferred C. Participant comments on the preferred search modes indicated that CENI was easy to use, provided better organization of health questions by topics, allowed users to narrow down to the most relevant contents quickly, and supported the exploratory navigation by non-experts or those unsure how to initiate their search. Conclusions: We presented a novel conjunctive exploratory navigation interface for consumer health information retrieval and navigation. Crowdsourcing permitted a carefully designed comparative search-interface evaluation to be completed in a timely and cost-effective manner with a relatively large number of participants recruited anonymously. Accounting for possible biases, our study has shown for the first time with crowdsourcing that the combination of exploratory navigation and lookup search is more effective than lookup search alone. ",
doi="10.2196/jmir.3111",
url="http://www.jmir.org/2014/2/e45/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24513593"
}


@Article{info:doi/10.2196/jmir.2842,
author="Schook, Milia Romane
and Linssen, Cilia
and Schramel, MNH Franz
and Festen, Jan
and Lammers, Ernst
and Smit, F. Egbert
and Postmus, E. Pieter
and Westerman, J. Marjan",
title="Why Do Patients and Caregivers Seek Answers From the Internet and Online Lung Specialists? A Qualitative Study",
journal="J Med Internet Res",
year="2014",
month="Feb",
day="04",
volume="16",
number="2",
pages="e37",
keywords="lung cancer",
keywords="patients",
keywords="caregivers",
keywords="website",
keywords="online lung specialists",
keywords="reasons",
keywords="Internet",
keywords="information needs",
keywords="coping",
keywords="qualitative",
abstract="Background: Since its launch in 2003, the Dutch Lung Cancer Information Center's (DLIC) website has become increasingly popular. The most popular page of the website is the section ``Ask the Physician'', where visitors can ask an online lung specialist questions anonymously and receive an answer quickly. Most questions were not only asked by lung cancer patients but also by their informal caregivers. Most questions concerned specific information about lung cancer. Objective: Our goal was to explore the reasons why lung cancer patients and caregivers search the Internet for information and ask online lung specialists questions on the DLIC's interactive page, ``Ask the Physician'', rather than consulting with their own specialist. Methods: This research consisted of a qualitative study with semistructured telephone interviews about medical information-seeking behavior (eg, information needs, reasons for querying online specialists). The sample comprised 5 lung cancer patients and 20 caregivers who posed a question on the interactive page of the DLIC website. Results: Respondents used the Internet and the DLIC website to look for lung cancer--related information (general/specific to their personal situation) and to cope with cancer. They tried to achieve a better understanding of the information given by their own specialist and wanted to be prepared for the treatment trajectory and disease course. This mode of information supply helped them cope and gave them emotional support. The interactive webpage was also used as a second opinion. The absence of face-to-face contact made respondents feel freer to ask for any kind of information. By being able to pose a question instantly and receiving a relatively quick reply from the online specialist to urgent questions, respondents felt an easing of their anxiety as they did not have to wait until the next consultation with their own specialist. Conclusions: The DLIC website with its interactive page is a valuable complementary mode of information supply and supportive care for lung cancer patients and caregivers. ",
doi="10.2196/jmir.2842",
url="http://www.jmir.org/2014/2/e37/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24496139"
}


@Article{info:doi/10.2196/jmir.2783,
author="Zhang, Yan",
title="The Effects of Preference for Information on Consumers' Online Health Information Search Behavior",
journal="J Med Internet Res",
year="2013",
month="Nov",
day="26",
volume="15",
number="11",
pages="e234",
keywords="preference for information",
keywords="health information",
keywords="consumer search behavior",
keywords="search engines",
abstract="Background: Preference for information is a personality trait that affects people's tendency to seek information in health-related situations. Prior studies have focused primarily on investigating its impact on patient-provider communication and on the implications for designing information interventions that prepare patients for medical procedures. Few studies have examined its impact on general consumers' interactions with Web-based search engines for health information or the implications for designing more effective health information search systems. Objective: This study intends to fill this gap by investigating the impact of preference for information on the search behavior of general consumers seeking health information, their perceptions of search tasks (representing information needs), and user experience with search systems. Methods: Forty general consumers who had previously searched for health information online participated in the study in our usability lab. Preference for information was measured using Miller's Monitor-Blunter Style Scale (MBSS) and the Krantz Health Opinion Survey-Information Scale (KHOS-I). Each participant completed four simulated health information search tasks: two look-up (fact-finding) and two exploratory. Their behaviors while interacting with the search systems were automatically logged and ratings of their perceptions of tasks and user experience with the systems were collected using Likert-scale questionnaires. Results: The MBSS showed low reliability with the participants (Monitoring subscale: Cronbach alpha=.53; Blunting subscale: Cronbach alpha=.35). Thus, no further analyses were performed based on the scale. KHOS-I had sufficient reliability (Cronbach alpha=.77). Participants were classified into low- and high-preference groups based on their KHOS-I scores. The high-preference group submitted significantly shorter queries when completing the look-up tasks (P=.02). The high-preference group made a significantly higher percentage of parallel movements in query reformulation than did the low-preference group (P=.04), whereas the low-preference group made a significantly higher percentage of new concept movements than the high-preference group when completing the exploratory tasks (P=.01). The high-preference group found the exploratory tasks to be significantly more difficult (P=.05) and the systems to be less useful (P=.04) than did the low-preference group. Conclusions: Preference for information has an impact on the search behavior of general consumers seeking health information. Those with a high preference were more likely to use more general queries when searching for specific factual information and to develop more complex mental representations of health concerns of an exploratory nature and try different combinations of concepts to explore these concerns. High-preference users were also more demanding on the system. Health information search systems should be tailored to fit individuals' information preferences. ",
doi="10.2196/jmir.2783",
url="http://www.jmir.org/2013/11/e234/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24284061"
}


@Article{info:doi/10.2196/jmir.2572,
author="McKibbon, Ann Kathleen
and Lokker, Cynthia
and Keepanasseril, Arun
and Wilczynski, L. Nancy
and Haynes, Brian R.",
title="Net Improvement of Correct Answers to Therapy Questions After PubMed Searches: Pre/Post Comparison",
journal="J Med Internet Res",
year="2013",
month="Nov",
day="08",
volume="15",
number="11",
pages="e243",
keywords="information services",
keywords="information storage and retrieval",
keywords="Internet",
keywords="Medline",
keywords="physicians",
keywords="primary health care",
abstract="Background: Clinicians search PubMed for answers to clinical questions although it is time consuming and not always successful. Objective: To determine if PubMed used with its Clinical Queries feature to filter results based on study quality would improve search success (more correct answers to clinical questions related to therapy). Methods: We invited 528 primary care physicians to participate, 143 (27.1\%) consented, and 111 (21.0\% of the total and 77.6\% of those who consented) completed the study. Participants answered 14 yes/no therapy questions and were given 4 of these (2 originally answered correctly and 2 originally answered incorrectly) to search using either the PubMed main screen or PubMed Clinical Queries narrow therapy filter via a purpose-built system with identical search screens. Participants also picked 3 of the first 20 retrieved citations that best addressed each question. They were then asked to re-answer the original 14 questions. Results: We found no statistically significant differences in the rates of correct or incorrect answers using the PubMed main screen or PubMed Clinical Queries. The rate of correct answers increased from 50.0\% to 61.4\% (95\% CI 55.0\%-67.8\%) for the PubMed main screen searches and from 50.0\% to 59.1\% (95\% CI 52.6\%-65.6\%) for Clinical Queries searches. These net absolute increases of 11.4\% and 9.1\%, respectively, included previously correct answers changing to incorrect at a rate of 9.5\% (95\% CI 5.6\%-13.4\%) for PubMed main screen searches and 9.1\% (95\% CI 5.3\%-12.9\%) for Clinical Queries searches, combined with increases in the rate of being correct of 20.5\% (95\% CI 15.2\%-25.8\%) for PubMed main screen searches and 17.7\% (95\% CI 12.7\%-22.7\%) for Clinical Queries searches. Conclusions: PubMed can assist clinicians answering clinical questions with an approximately 10\% absolute rate of improvement in correct answers. This small increase includes more correct answers partially offset by a decrease in previously correct answers. ",
doi="10.2196/jmir.2572",
url="http://www.jmir.org/2013/11/e243/",
url="http://www.ncbi.nlm.nih.gov/pubmed/24217329"
}


@Article{info:doi/10.2196/jmir.2632,
author="Dunne, Suzanne
and Cummins, Maria Niamh
and Hannigan, Ailish
and Shannon, Bill
and Dunne, Colum
and Cullen, Walter",
title="A Method for the Design and Development of Medical or Health Care Information Websites to Optimize Search Engine Results Page Rankings on Google",
journal="J Med Internet Res",
year="2013",
month="Aug",
day="27",
volume="15",
number="8",
pages="e183",
keywords="health care information",
keywords="patient education",
keywords="Google",
keywords="Internet",
keywords="medical informatics",
keywords="generic drugs",
keywords="website development",
keywords="quality assessment",
abstract="Background: The Internet is a widely used source of information for patients searching for medical/health care information. While many studies have assessed existing medical/health care information on the Internet, relatively few have examined methods for design and delivery of such websites, particularly those aimed at the general public. Objective: This study describes a method of evaluating material for new medical/health care websites, or for assessing those already in existence, which is correlated with higher rankings on Google's Search Engine Results Pages (SERPs). Methods: A website quality assessment (WQA) tool was developed using criteria related to the quality of the information to be contained in the website in addition to an assessment of the readability of the text. This was retrospectively applied to assess existing websites that provide information about generic medicines. The reproducibility of the WQA tool and its predictive validity were assessed in this study. Results: The WQA tool demonstrated very high reproducibility (intraclass correlation coefficient=0.95) between 2 independent users. A moderate to strong correlation was found between WQA scores and rankings on Google SERPs. Analogous correlations were seen between rankings and readability of websites as determined by Flesch Reading Ease and Flesch-Kincaid Grade Level scores. Conclusions: The use of the WQA tool developed in this study is recommended as part of the design phase of a medical or health care information provision website, along with assessment of readability of the material to be used. This may ensure that the website performs better on Google searches. The tool can also be used retrospectively to make improvements to existing websites, thus, potentially enabling better Google search result positions without incurring the costs associated with Search Engine Optimization (SEO) professionals or paid promotion. ",
doi="10.2196/jmir.2632",
url="http://www.jmir.org/2013/8/e183/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23981848"
}


@Article{info:doi/10.2196/jmir.2624,
author="Shariff, Z. Salimah
and Bejaimal, AD Shayna
and Sontrop, M. Jessica
and Iansavichus, V. Arthur
and Haynes, Brian R.
and Weir, A. Matthew
and Garg, X. Amit",
title="Retrieving Clinical Evidence: A Comparison of PubMed and Google Scholar for Quick Clinical Searches",
journal="J Med Internet Res",
year="2013",
month="Aug",
day="15",
volume="15",
number="8",
pages="e164",
keywords="information dissemination/methods",
keywords="information storage and retrieval",
keywords="medical",
keywords="library science",
keywords="PubMed",
keywords="Google Scholar",
keywords="nephrology",
abstract="Background: Physicians frequently search PubMed for information to guide patient care. More recently, Google Scholar has gained popularity as another freely accessible bibliographic database. Objective: To compare the performance of searches in PubMed and Google Scholar. Methods: We surveyed nephrologists (kidney specialists) and provided each with a unique clinical question derived from 100 renal therapy systematic reviews. Each physician provided the search terms they would type into a bibliographic database to locate evidence to answer the clinical question. We executed each of these searches in PubMed and Google Scholar and compared results for the first 40 records retrieved (equivalent to 2 default search pages in PubMed). We evaluated the recall (proportion of relevant articles found) and precision (ratio of relevant to nonrelevant articles) of the searches performed in PubMed and Google Scholar. Primary studies included in the systematic reviews served as the reference standard for relevant articles. We further documented whether relevant articles were available as free full-texts. Results: Compared with PubMed, the average search in Google Scholar retrieved twice as many relevant articles (PubMed: 11\%; Google Scholar: 22\%; P<.001). Precision was similar in both databases (PubMed: 6\%; Google Scholar: 8\%; P=.07). Google Scholar provided significantly greater access to free full-text publications (PubMed: 5\%; Google Scholar: 14\%; P<.001). Conclusions: For quick clinical searches, Google Scholar returns twice as many relevant articles as PubMed and provides greater access to free full-text articles. ",
doi="10.2196/jmir.2624",
url="http://www.jmir.org/2013/8/e164/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23948488"
}


@Article{info:doi/10.2196/jmir.2436,
author="Kritz, Marlene
and Gschwandtner, Manfred
and Stefanov, Veronika
and Hanbury, Allan
and Samwald, Matthias",
title="Utilization and Perceived Problems of Online Medical Resources and Search Tools Among Different Groups of European Physicians",
journal="J Med Internet Res",
year="2013",
month="Jun",
day="26",
volume="15",
number="6",
pages="e122",
keywords="information seeking behavior",
keywords="physicians",
keywords="Internet",
keywords="search engine",
keywords="information quality",
keywords="language barriers",
abstract="Background: There is a large body of research suggesting that medical professionals have unmet information needs during their daily routines. Objective: To investigate which online resources and tools different groups of European physicians use to gather medical information and to identify barriers that prevent the successful retrieval of medical information from the Internet. Methods: A detailed Web-based questionnaire was sent out to approximately 15,000 physicians across Europe and disseminated through partner websites. 500 European physicians of different levels of academic qualification and medical specialization were included in the analysis. Self-reported frequency of use of different types of online resources, perceived importance of search tools, and perceived search barriers were measured. Comparisons were made across different levels of qualification (qualified physicians vs physicians in training, medical specialists without professorships vs medical professors) and specialization (general practitioners vs specialists). Results: Most participants were Internet-savvy, came from Austria (43\%, 190/440) and Switzerland (31\%, 137/440), were above 50 years old (56\%, 239/430), stated high levels of medical work experience, had regular patient contact and were employed in nonacademic health care settings (41\%, 177/432). All groups reported frequent use of general search engines and cited ``restricted accessibility to good quality information'' as a dominant barrier to finding medical information on the Internet. Physicians in training reported the most frequent use of Wikipedia (56\%, 31/55). Specialists were more likely than general practitioners to use medical research databases (68\%, 185/274 vs 27\%, 24/88; $\chi$22=44.905, P<.001). General practitioners were more likely than specialists to report ``lack of time'' as a barrier towards finding information on the Internet (59\%, 50/85 vs 43\%, 111/260; $\chi$21=7.231, P=.007) and to restrict their search by language (48\%, 43/89 vs 35\%, 97/278; $\chi$21=5.148, P=.023). They frequently consult general health websites (36\%, 31/87 vs 19\%, 51/269; $\chi$22=12.813, P=.002) and online physician network communities (17\%, 15/86, $\chi$22=9.841 vs 6\%, 17/270, P<.001). Conclusions: The reported inaccessibility of relevant, trustworthy resources on the Internet and frequent reliance on general search engines and social media among physicians require further attention. Possible solutions may be increased governmental support for the development and popularization of user-tailored medical search tools and open access to high-quality content for physicians. The potential role of collaborative tools in providing the psychological support and affirmation normally given by medical colleagues needs further consideration. Tools that speed up quality evaluation and aid selection of relevant search results need to be identified. In order to develop an adequate search tool, a differentiated approach considering the differing needs of physician subgroups may be beneficial. ",
doi="10.2196/jmir.2436",
url="http://www.jmir.org/2013/6/e122/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23803299"
}


@Article{info:doi/10.2196/jmir.2614,
author="Yom-Tov, Elad
and Gabrilovich, Evgeniy",
title="Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries",
journal="J Med Internet Res",
year="2013",
month="Jun",
day="18",
volume="15",
number="6",
pages="e124",
keywords="machine learning",
keywords="information retrieval",
keywords="side effects",
keywords="infoveillance",
keywords="infodemiology",
abstract="Background: Postmarket drug safety surveillance largely depends on spontaneous reports by patients and health care providers; hence, less common adverse drug reactions---especially those caused by long-term exposure, multidrug treatments, or those specific to special populations---often elude discovery. Objective: Here we propose a low cost, fully automated method for continuous monitoring of adverse drug reactions in single drugs and in combinations thereof, and demonstrate the discovery of heretofore-unknown ones. Methods: We used aggregated search data of large populations of Internet users to extract information related to drugs and adverse reactions to them, and correlated these data over time. We further extended our method to identify adverse reactions to combinations of drugs. Results: We validated our method by showing high correlations of our findings with known adverse drug reactions (ADRs). However, although acute early-onset drug reactions are more likely to be reported to regulatory agencies, we show that less acute later-onset ones are better captured in Web search queries. Conclusions: Our method is advantageous in identifying previously unknown adverse drug reactions. These ADRs should be considered as candidates for further scrutiny by medical regulatory authorities, for example, through phase 4 trials. ",
doi="10.2196/jmir.2614",
url="http://www.jmir.org/2013/6/e124/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23778053"
}


@Article{info:doi/10.2196/jmir.2324,
author="Hua, My
and Alfi, Mina
and Talbot, Prue",
title="Health-Related Effects Reported by Electronic Cigarette Users in Online Forums",
journal="J Med Internet Res",
year="2013",
month="Apr",
day="08",
volume="15",
number="4",
pages="e59",
keywords="Electronic cigarettes",
keywords="e-cigarettes",
keywords="electronic nicotine delivery devices",
keywords="ENDS",
keywords="health effects",
keywords="nicotine",
keywords="harm reduction",
keywords="symptoms",
keywords="Internet",
abstract="Background: The health effects caused by electronic cigarette (e-cigarette) use are not well understood. Objective: Our purpose was to document the positive and negative short-term health effects produced by e-cigarette use through an analysis of original posts from three online e-cigarettes forums. Methods: Data were collected into Microsoft Access databases and analyzed using Cytoscape association graphics, frequency distributions, and interactomes to determine the number and type of health effects reported, the organ systems affected the frequency of specific effects, and systems interactions. Results: A total of 405 different symptoms due to e-cigarette use were reported from three forums. Of these, 78 were positive, 326 were negative, and one was neutral. While the reported health effects were similar in all three forums, the forum with the most posts was analyzed in detail. Effects, which were reported for 12 organ systems/anatomical regions, occurred most often in the mouth and throat and in the respiratory, neurological, sensory, and digestive systems. Users with negative symptoms often reported more than one symptom, and in these cases interactions were often seen between systems, such as the circulatory and neurological systems. Positive effects usually occurred singly and most frequently affected the respiratory system. Conclusions: This is the first compilation and analysis of the health effects reported by e-cigarette users in online forums. These data show that e-cigarette use can have wide ranging positive and negative effects and that online forums provide a useful resource for examining how e-cigarette use affects health. ",
doi="10.2196/jmir.2324",
url="http://www.jmir.org/2013/4/e59/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23567935"
}


@Article{info:doi/10.2196/jmir.2353,
author="Batch, Yamen
and Yusof, Mohd Maryati
and Noah, Azman Shahrul",
title="ICDTag: A Prototype for a Web-Based System for Organizing Physician-Written Blog Posts Using a Hybrid Taxonomy-Folksonomy Approach",
journal="J Med Internet Res",
year="2013",
month="Feb",
day="27",
volume="15",
number="2",
pages="e41",
keywords="Web-based systems",
keywords="medical",
keywords="physician",
keywords="blogs",
keywords="folksonomy",
keywords="taxonomy",
keywords="collaborative tagging",
keywords="ICD-11",
abstract="Background: Medical blogs have emerged as new media, extending to a wider range of medical audiences, including health professionals and patients to share health-related information. However, extraction of quality health-related information from medical blogs is challenging primarily because these blogs lack systematic methods to organize their posts. Medical blogs can be categorized according to their author into (1) physician-written blogs, (2) nurse-written blogs, and (3) patient-written blogs. This study focuses on how to organize physician-written blog posts that discuss disease-related issues and how to extract quality information from these posts. Objective: The goal of this study was to create and implement a prototype for a Web-based system, called ICDTag, based on a hybrid taxonomy--folksonomy approach that follows a combination of a taxonomy classification schemes and user-generated tags to organize physician-written blog posts and extract information from these posts. Methods: First, the design specifications for the Web-based system were identified. This system included two modules: (1) a blogging module that was implemented as one or more blogs, and (2) an aggregator module that aggregated posts from different blogs into an aggregator website. We then developed a prototype for this system in which the blogging module included two blogs, the cardiology blog and the gastroenterology blog. To analyze the usage patterns of the prototype, we conducted an experiment with data provided by cardiologists and gastroenterologists. Next, we conducted two evaluation types: (1) an evaluation of the ICDTag blog, in which the browsing functionalities of the blogging module were evaluated from the end-user's perspective using an online questionnaire, and (2) an evaluation of information quality, in which the quality of the content on the aggregator website was assessed from the perspective of medical experts using an emailed questionnaire. Results: Participants of this experiment included 23 cardiologists and 24 gastroenterologists. Positive evaluations on the main functions and the organization of information on the ICDTag blogs were given by 18 of the participants via an online questionnaire. These results supported our hypothesis that the use of a taxonomy-folksonomy structure has significant potential to improve the organization of information in physician-written blogs. The quality of the content on the aggregator website was assessed by 3 cardiology experts and 3 gastroenterology experts via an email questionnaire. The results of this questionnaire demonstrated that the experts considered the aggregated tags and categories semantically related to the posts' content. Conclusions: This study demonstrated that applying the hybrid taxonomy--folksonomy approach to physician-written blogs that discuss disease-related issues has valuable potential to make these blogs a more organized and systematic medium and supports the extraction of quality information from their posts. Thus, it is worthwhile to develop more mature systems that make use of the hybrid approach to organize posts in physician-written blogs. ",
doi="10.2196/jmir.2353",
url="http://www.jmir.org/2013/2/e41/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23470419"
}


@Article{info:doi/10.2196/jmir.2105,
author="Jeffery, Rebecca
and Navarro, Tamara
and Lokker, Cynthia
and Haynes, Brian R.
and Wilczynski, L. Nancy
and Farjou, George",
title="How Current Are Leading Evidence-Based Medical Textbooks? An Analytic Survey of Four Online Textbooks",
journal="J Med Internet Res",
year="2012",
month="Dec",
day="10",
volume="14",
number="6",
pages="e175",
keywords="databases, bibliographic",
keywords="medical informatics",
keywords="evidence-based medicine",
abstract="Background: The consistency of treatment recommendations of evidence-based medical textbooks with more recently published evidence has not been investigated to date. Inconsistencies could affect the quality of medical care. Objective: To determine the frequency with which topics in leading online evidence-based medical textbooks report treatment recommendations consistent with more recently published research evidence. Methods: Summarized treatment recommendations in 200 clinical topics (ie, disease states) covered in four evidence-based textbooks--UpToDate, Physicians' Information Education Resource (PIER), DynaMed, and Best Practice--were compared with articles identified in an evidence rating service (McMaster Premium Literature Service, PLUS) since the date of the most recent topic updates in each textbook. Textbook treatment recommendations were compared with article results to determine if the articles provided different, new conclusions. From these findings, the proportion of topics which potentially require updating in each textbook was calculated. Results: 478 clinical topics were assessed for inclusion to find 200 topics that were addressed by all four textbooks. The proportion of topics for which there was 1 or more recently published articles found in PLUS with evidence that differed from the textbooks' treatment recommendations was 23\% (95\% CI 17-29\%) for DynaMed, 52\% (95\% CI 45-59\%) for UpToDate, 55\% (95\% CI 48-61\%) for PIER, and 60\% (95\% CI 53-66\%) for Best Practice ($\chi$23=65.3, P<.001). The time since the last update for each textbook averaged from 170 days (range 131-209) for DynaMed, to 488 days (range 423-554) for PIER (P<.001 across all textbooks). Conclusions: In online evidence-based textbooks, the proportion of topics with potentially outdated treatment recommendations varies substantially. ",
doi="10.2196/jmir.2105",
url="http://www.jmir.org/2012/6/e175/",
url="http://www.ncbi.nlm.nih.gov/pubmed/23220465"
}


@Article{info:doi/10.2196/jmir.2021,
author="Agoritsas, Thomas
and Merglen, Arnaud
and Courvoisier, S. Delphine
and Combescure, Christophe
and Garin, Nicolas
and Perrier, Arnaud
and Perneger, V. Thomas",
title="Sensitivity and Predictive Value of 15 PubMed Search Strategies to Answer Clinical Questions Rated Against Full Systematic Reviews",
journal="J Med Internet Res",
year="2012",
month="Jun",
day="12",
volume="14",
number="3",
pages="e85",
keywords="Evidence-based medicine",
keywords="information retrieval",
keywords="medical literature",
keywords="search strategy",
keywords="PubMed",
keywords="Medline",
keywords="clinical queries",
keywords="search filters",
keywords="sensitivity",
keywords="recall",
keywords="positive predictive value",
keywords="precision",
abstract="Background: Clinicians perform searches in PubMed daily, but retrieving relevant studies is challenging due to the rapid expansion of medical knowledge. Little is known about the performance of search strategies when they are applied to answer specific clinical questions. Objective: To compare the performance of 15 PubMed search strategies in retrieving relevant clinical trials on therapeutic interventions. Methods: We used Cochrane systematic reviews to identify relevant trials for 30 clinical questions. Search terms were extracted from the abstract using a predefined procedure based on the population, interventions, comparison, outcomes (PICO) framework and combined into queries. We tested 15 search strategies that varied in their query (PIC or PICO), use of PubMed's Clinical Queries therapeutic filters (broad or narrow), search limits, and PubMed links to related articles. We assessed sensitivity (recall) and positive predictive value (precision) of each strategy on the first 2 PubMed pages (40 articles) and on the complete search output. Results: The performance of the search strategies varied widely according to the clinical question. Unfiltered searches and those using the broad filter of Clinical Queries produced large outputs and retrieved few relevant articles within the first 2 pages, resulting in a median sensitivity of only 10\%--25\%. In contrast, all searches using the narrow filter performed significantly better, with a median sensitivity of about 50\% (all P < .001 compared with unfiltered queries) and positive predictive values of 20\%--30\% (P < .001 compared with unfiltered queries). This benefit was consistent for most clinical questions. Searches based on related articles retrieved about a third of the relevant studies. Conclusions: The Clinical Queries narrow filter, along with well-formulated queries based on the PICO framework, provided the greatest aid in retrieving relevant clinical trials within the 2 first PubMed pages. These results can help clinicians apply effective strategies to answer their questions at the point of care. ",
doi="10.2196/jmir.2021",
url="http://www.jmir.org/2012/3/e85/",
url="http://www.ncbi.nlm.nih.gov/pubmed/22693047"
}


@Article{info:doi/10.2196/jmir.1943,
author="Wang, Liupu
and Wang, Juexin
and Wang, Michael
and Li, Yong
and Liang, Yanchun
and Xu, Dong",
title="Using Internet Search Engines to Obtain Medical Information: A Comparative Study",
journal="J Med Internet Res",
year="2012",
month="May",
day="16",
volume="14",
number="3",
pages="e74",
keywords="Internet search",
keywords="page rank",
keywords="Google, Yahoo!, Bing, Ask.com",
keywords="medical information",
keywords="health information seeking",
keywords="breast cancer",
keywords="SNOMED CT",
keywords="user experience evaluation",
keywords="usability testing",
keywords="hallway testing",
keywords="software engineering",
abstract="Background: The Internet has become one of the most important means to obtain health and medical information. It is often the first step in checking for basic information about a disease and its treatment. The search results are often useful to general users. Various search engines such as Google, Yahoo!, Bing, and Ask.com can play an important role in obtaining medical information for both medical professionals and lay people. However, the usability and effectiveness of various search engines for medical information have not been comprehensively compared and evaluated. Objective: To compare major Internet search engines in their usability of obtaining medical and health information. Methods: We applied usability testing as a software engineering technique and a standard industry practice to compare the four major search engines (Google, Yahoo!, Bing, and Ask.com) in obtaining health and medical information. For this purpose, we searched the keyword breast cancer in Google, Yahoo!, Bing, and Ask.com and saved the results of the top 200 links from each search engine. We combined nonredundant links from the four search engines and gave them to volunteer users in an alphabetical order. The volunteer users evaluated the websites and scored each website from 0 to 10 (lowest to highest) based on the usefulness of the content relevant to breast cancer. A medical expert identified six well-known websites related to breast cancer in advance as standards. We also used five keywords associated with breast cancer defined in the latest release of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and analyzed their occurrence in the websites. Results: Each search engine provided rich information related to breast cancer in the search results. All six standard websites were among the top 30 in search results of all four search engines. Google had the best search validity (in terms of whether a website could be opened), followed by Bing, Ask.com, and Yahoo!. The search results highly overlapped between the search engines, and the overlap between any two search engines was about half or more. On the other hand, each search engine emphasized various types of content differently. In terms of user satisfaction analysis, volunteer users scored Bing the highest for its usefulness, followed by Yahoo!, Google, and Ask.com. Conclusions: Google, Yahoo!, Bing, and Ask.com are by and large effective search engines for helping lay users get health and medical information. Nevertheless, the current ranking methods have some pitfalls and there is room for improvement to help users get more accurate and useful information. We suggest that search engine users explore multiple search engines to search different types of health information and medical knowledge for their own needs and get a professional consultation if necessary. ",
doi="10.2196/jmir.1943",
url="http://www.jmir.org/2012/3/e74/",
url="http://www.ncbi.nlm.nih.gov/pubmed/22672889"
}


@Article{info:doi/10.2196/jmir.1683,
author="Grad, Roland
and Pluye, Pierre
and Johnson-Lafleur, Janique
and Granikov, Vera
and Shulha, Michael
and Bartlett, Gillian
and Marlow, Bernard",
title="Do Family Physicians Retrieve Synopses of Clinical Research Previously Read as Email Alerts?",
journal="J Med Internet Res",
year="2011",
month="Nov",
day="30",
volume="13",
number="4",
pages="e101",
keywords="Electronic mail",
keywords="clinical email channels",
keywords="information retrieval",
keywords="physicians, family",
abstract="Background: A synopsis of new clinical research highlights important aspects of one study in a brief structured format. When delivered as email alerts, synopses enable clinicians to become aware of new developments relevant for practice. Once read, a synopsis can become a known item of clinical information. In time-pressured situations, remembering a known item may facilitate information retrieval by the clinician. However, exactly how synopses first delivered as email alerts influence retrieval at some later time is not known. Objectives: We examined searches for clinical information in which a synopsis previously read as an email alert was retrieved (defined as a dyad). Our study objectives were to (1) examine whether family physicians retrieved synopses they previously read as email alerts and then to (2) explore whether family physicians purposefully retrieved these synopses.  Methods: We conducted a mixed-methods study in which a qualitative multiple case study explored the retrieval of email alerts within a prospective longitudinal cohort of practicing family physicians. Reading of research-based synopses was tracked in two contexts: (1) push, meaning to read on email and (2) pull, meaning to read after retrieval from one electronic knowledge resource. Dyads, defined as synopses first read as email alerts and subsequently retrieved in a search of a knowledge resource, were prospectively identified. Participants were interviewed about all of their dyads. Outcomes were the total number of dyads and their type.  Results: Over a period of 341 days, 194 unique synopses delivered to 41 participants resulted in 4937 synopsis readings. In all, 1205 synopses were retrieved over an average of 320 days. Of the 1205 retrieved synopses, 21 (1.7\%) were dyads made by 17 family physicians. Of the 1205 retrieved synopses, 6 (0.5\%) were known item type dyads. However, dyads also occurred serendipitously. Conclusion: In the single knowledge resource we studied, email alerts containing research-based synopses were rarely retrieved. Our findings help us to better understand the effect of push on pull and to improve the integration of research-based information within electronic resources for clinicians. ",
doi="10.2196/jmir.1683",
url="http://www.jmir.org/2011/4/e101/",
url="http://www.ncbi.nlm.nih.gov/pubmed/22130465"
}


@Article{info:doi/10.2196/jmir.1826,
author="Shaikh, Nader
and Badgett, G. Robert
and Pi, Mina
and Wilczynski, L. Nancy
and McKibbon, Ann K.
and Ketchum, M. Andrea
and Haynes, Brian R.",
title="Development and Validation of Filters for the Retrieval of Studies of Clinical Examination From Medline",
journal="J Med Internet Res",
year="2011",
month="Oct",
day="19",
volume="13",
number="4",
pages="e82",
keywords="Medline",
keywords="filter",
keywords="hedge",
keywords="clinical examination",
keywords="recursive partitioning",
abstract="Background: Efficiently finding clinical examination studies---studies that quantify the value of symptoms and signs in the diagnosis of disease---is becoming increasingly difficult. Filters developed to retrieve studies of diagnosis from Medline lack specificity because they also retrieve large numbers of studies on the diagnostic value of imaging and laboratory tests. Objective: The objective was to develop filters for retrieving clinical examination studies from Medline. Methods: We developed filters in a training dataset and validated them in a testing database. We created the training database by hand searching 161 journals (n = 52,636 studies). We evaluated the recall and precision of 65 candidate single-term filters in identifying studies that reported the sensitivity and specificity of symptoms or signs in the training database. To identify best combinations of these search terms, we used recursive partitioning. The best-performing filters in the training database as well as 13 previously developed filters were evaluated in a testing database (n = 431,120 studies). We also examined the impact of examining reference lists of included articles on recall. Results: In the training database, the single-term filters with the highest recall (95\%) and the highest precision (8.4\%) were diagnosis[subheading] and ``medical history taking''[MeSH], respectively. The multiple-term filter developed using recursive partitioning (the RP filter) had a recall of 100\% and a precision of 89\% in the training database. In the testing database, the Haynes-2004-Sensitive filter (recall 98\%, precision 0.13\%) and the RP filter (recall 89\%, precision 0.52\%) showed the best performance. The recall of these two filters increased to 99\% and 94\% respectively with review of the reference lists of the included articles. Conclusions: Recursive partitioning appears to be a useful method of developing search filters. The empirical search filters proposed here can assist in the retrieval of clinical examination studies from Medline; however, because of the low precision of the search strategies, retrieving relevant studies remains challenging. Improving precision may require systematic changes in the tagging of articles by the National Library of Medicine. ",
doi="10.2196/jmir.1826",
url="http://www.jmir.org/2011/4/e82/",
url="http://www.ncbi.nlm.nih.gov/pubmed/22011384"
}


@Article{info:doi/10.2196/jmir.1539,
author="Ketchum, M. Andrea
and Saleh, A. Ahlam
and Jeong, Kwonho",
title="Type of Evidence Behind Point-of-Care Clinical Information Products: A Bibliometric Analysis",
journal="J Med Internet Res",
year="2011",
month="Feb",
day="18",
volume="13",
number="1",
pages="e21",
keywords="Databases, Factual",
keywords="Bibliometrics",
keywords="Medical Informatics",
keywords="Evidence-based Medicine",
abstract="Background: Point-of-care (POC) products are widely used as information reference tools in the clinical setting. Although usability, scope of coverage, ability to answer clinical questions, and impact on health outcomes have been studied, no comparative analysis of the characteristics of the references, the evidence for the content, in POC products is available. Objective: The objective of this study was to compare the type of evidence behind five POC clinical information products. Methods: This study is a comparative bibliometric analysis of references cited in monographs in POC products. Five commonly used products served as subjects for the study: ACP PIER, Clinical Evidence, DynaMed, FirstCONSULT, and UpToDate. The four clinical topics examined to identify content in the products were asthma, hypertension, hyperlipidemia, and carbon monoxide poisoning. Four indicators were measured: distribution of citations, type of evidence, product currency, and citation overlap. The type of evidence was determined based primarily on the publication type found in the MEDLINE bibliographic record, as well as the Medical Subject Headings (MeSH), both assigned by the US National Library of Medicine. MeSH is the controlled vocabulary used for indexing articles in MEDLINE/PubMed. Results: FirstCONSULT had the greatest proportion of references with higher levels of evidence publication types such as systematic review and randomized controlled trial (137/153, 89.5\%), although it contained the lowest total number of references (153/2330, 6.6\%). DynaMed had the largest total number of references (1131/2330, 48.5\%) and the largest proportion of current (2007-2009) references (170/1131, 15\%). The distribution of references cited for each topic varied between products. For example, asthma had the most references listed in DynaMed, Clinical Evidence, and FirstCONSULT, while hypertension had the most references in UpToDate and ACP PIER. An unexpected finding was that the rate of citation overlap was less than 1\% for each topic across all five products. Conclusions: Differences between POC products are revealed by examining the references cited in the monographs themselves. Citation analysis extended to include key content indicators can be used to compare the evidence levels of the literature supporting the content found in POC products.  ",
doi="10.2196/jmir.1539",
url="http://www.jmir.org/2011/1/e21/"
}