@Article{info:doi/10.2196/53741,
author="Siira, Elin
and Johansson, Hanna
and Nygren, Jens",
title="Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="6",
volume="27",
pages="e53741",
keywords="scoping review",
keywords="artificial intelligence",
keywords="AI",
keywords="medical history taking",
keywords="triage",
keywords="health care",
keywords="automation",
abstract="Background: The integration of artificial intelligence (AI) systems for automating medical history taking and triage can significantly enhance patient flow in health care systems. Despite the promising performance of numerous AI studies, only a limited number of these systems have been successfully integrated into routine health care practice. To elucidate how AI systems can create value in this context, it is crucial to identify the current state of knowledge, including the readiness of these systems, the facilitators of and barriers to their implementation, and the perspectives of various stakeholders involved in their development and deployment. Objective: This study aims to map and summarize empirical research on AI systems designed for automating medical history taking and triage in health care settings. Methods: The study was conducted following the framework proposed by Arksey and O'Malley and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search of 5 databases---PubMed, CINAHL, PsycINFO, Scopus, and Web of Science---was performed. A detailed protocol was established before the review to ensure methodological rigor. Results: A total of 1248 research publications were identified and screened. Of these, 86 (6.89\%) met the eligibility criteria. Notably, most (n=63, 73\%) studies were published between 2020 and 2022, with a significant concentration on emergency care (n=32, 37\%). Other clinical contexts included radiology (n=12, 14\%) and primary care (n=6, 7\%). Many (n=15, 17\%) studies did not specify a clinical context. Most (n=31, 36\%) studies used retrospective designs, while others (n=34, 40\%) did not specify their methodologies. The predominant type of AI system identified was the hybrid model (n=68, 79\%), with forecasting (n=40, 47\%) and recognition (n=36, 42\%) being the most common tasks performed. While most (n=70, 81\%) studies included patient populations, only 1 (1\%) study investigated patients' views on AI-based medical history taking and triage, and 2 (2\%) studies considered health care professionals' perspectives. Furthermore, only 6 (7\%) studies validated or demonstrated AI systems in relevant clinical settings through real-time model testing, workflow implementation, clinical outcome evaluation, or integration into practice. Most (n=76, 88\%) studies were concerned with the prototyping, development, or validation of AI systems. In total, 4 (5\%) studies were reviews of several empirical studies conducted in different clinical settings. The facilitators and barriers to AI system implementation were categorized into 4 themes: technical aspects, contextual and cultural considerations, end-user engagement, and evaluation processes. Conclusions: This review highlights current trends, stakeholder perspectives, stages of innovation development, and key influencing factors related to implementing AI systems in health care. The identified literature gaps regarding stakeholder perspectives and the limited research on AI systems for automating medical history taking and triage indicate significant opportunities for further investigation and development in this evolving field. ",
doi="10.2196/53741",
url="https://www.jmir.org/2025/1/e53741"
}


@Article{info:doi/10.2196/60655,
author="Craamer, Casper
and Timmers, Thomas
and Siebelt, Michiel
and Kool, Bertijn Rudolf
and Diekerhof, Carel
and Caron, Jacob Jan
and Gosens, Taco
and van der Weegen, Walter",
title="Completion Rate and Satisfaction With Online Computer-Assisted History Taking Questionnaires in Orthopedics: Multicenter Implementation Report",
journal="JMIR Med Inform",
year="2024",
month="Nov",
day="13",
volume="12",
pages="e60655",
keywords="computer-assisted history taking",
keywords="history taking",
keywords="digital medical interview",
keywords="orthopedics",
keywords="digital health",
keywords="computer-assisted",
keywords="cohort study",
keywords="orthopedic",
keywords="outpatient",
keywords="satisfaction",
keywords="patient engagement",
keywords="medical record",
abstract="Background: Collecting the medical history during a first outpatient consultation plays an important role in making a diagnosis. However, it is a time-consuming process, and time is scarce in today's health care environment. The computer-assisted history taking (CAHT) systems allow patients to share their medical history electronically before their visit. Although multiple advantages of CAHT have been demonstrated, adoption in everyday medical practice remains low, which has been attributed to various barriers. Objective: This study aimed to implement a CAHT questionnaire for orthopedic patients in preparation for their first outpatient consultation and analyze its completion rate and added value. Methods: A multicenter implementation study was conducted in which all patients who were referred to the orthopedic department were invited to self-complete the CAHT questionnaire. The primary outcome of the study is the completion rate of the questionnaire. Secondary outcomes included patient and physician satisfaction. These were assessed via surveys and semistructured interviews. Implementation (Results): In total, 5321 patients were invited, and 4932 (92.7\%) fully completed the CAHT questionnaire between April 2022 and July 2022. On average, participants (n=224) rated the easiness of completing the questionnaire at 8.0 (SD 1.9; 0?10 scale) and the satisfaction of the consult at 8.0 (SD 1.7; 0?10 scale). Satisfaction with the outpatient consultation was higher in cases where the given answers were used by the orthopedic surgeon during this consultation (median 8.3, IQR 8.0?9.1 vs median 8.0, IQR 7.0?8.5; P<.001). Physicians (n=15) scored the average added value as 7.8 (SD 1.7; 0?10 scale) and unanimously recognized increased efficiency, better patient engagement, and better medical record completeness. Implementing the patient's answers into the electronic health record was deemed necessary. Conclusions: In this study, we have shown that previously recognized barriers to implementing and adapting CAHT can now be effectively overcome. We demonstrated that almost all patients completed the CAHT questionnaire. This results in reported improvements in both the efficiency and personalization of outpatient consultations. Given the pressing need for personalized health care delivery in today's time-constrained medical environment, we recommend implementing CAHT systems in routine medical practice. ",
doi="10.2196/60655",
url="https://medinform.jmir.org/2024/1/e60655"
}


@Article{info:doi/10.2196/55164,
author="Nguyen, Hoang Michelle
and Sedoc, Jo{\~a}o
and Taylor, Overby Casey",
title="Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis",
journal="J Med Internet Res",
year="2024",
month="Sep",
day="30",
volume="26",
pages="e55164",
keywords="family health history",
keywords="chatbots",
keywords="conversational agents",
keywords="digital health tools",
keywords="usability",
keywords="engagement",
keywords="report usefulness",
keywords="evaluation",
keywords="crowdsourcing",
keywords="mixed methods",
abstract="Background: Family health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States. Objective: This study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx. Methods: This mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method. KIT's design was optimized to reduce user burden. We recruited and randomized individuals from 2 crowdsourced platforms to 1 of the 2 FHx methods. All participants were asked to complete a questionnaire to assess the method's usability, the usefulness of a report summarizing their experience, user-desired chatbot enhancements, and general user experience. Engagement was studied using log data collected by the methods. We used qualitative findings from analyzing free-text comments to supplement the primary quantitative results. Results: Participants randomized to KIT reported higher usability than those randomized to the form, with a mean System Usability Scale score of 80.2 versus 61.9 (P<.001), respectively. The engagement analysis reflected design differences in the onboarding process. KIT users spent less time entering FHx information and reported more conditions than form users (mean 5.90 vs 7.97 min; P=.04; and mean 7.8 vs 10.1 conditions; P=.04). Both KIT and form users somewhat agreed that the report was useful (Likert scale ratings of 4.08 and 4.29, respectively). Among desired enhancements, personalization was the highest-rated feature (188/205, 91.7\% rated medium- to high-priority). Qualitative analyses revealed positive and negative characteristics of both KIT and the form-based method. Among respondents randomized to KIT, most indicated it was easy to use and navigate and that they could respond to and understand user prompts. Negative comments addressed KIT's personality, conversational pace, and ability to manage errors. For KIT and form respondents, qualitative results revealed common themes, including a desire for more information about conditions and a mutual appreciation for the multiple-choice button response format. Respondents also said they wanted to report health information beyond KIT's prompts (eg, personal health history) and for KIT to provide more personalized responses. Conclusions: We showed that KIT provided a usable way to collect FHx. We also identified design considerations to improve chatbot-based FHx data collection: First, the final report summarizing the FHx collection experience should be enhanced to provide more value for patients. Second, the onboarding chatbot prompt may impact data quality and should be carefully considered. Finally, we highlighted several areas that could be improved by moving from a flow-based chatbot to a large language model implementation strategy. ",
doi="10.2196/55164",
url="https://www.jmir.org/2024/1/e55164",
url="http://www.ncbi.nlm.nih.gov/pubmed/39348188"
}


@Article{info:doi/10.2196/37028,
author="Hong, Grace
and Smith, Margaret
and Lin, Steven",
title="The AI Will See You Now: Feasibility and Acceptability of a Conversational AI Medical Interviewing System",
journal="JMIR Form Res",
year="2022",
month="Jun",
day="27",
volume="6",
number="6",
pages="e37028",
keywords="artificial intelligence",
keywords="feasibility studies",
keywords="patient acceptance of health care",
keywords="diagnostic errors",
keywords="patient-generated health data",
keywords="clinical",
keywords="medical history",
keywords="healthcare",
keywords="health care",
abstract="Background: Primary care physicians (PCPs) are often limited in their ability to collect detailed medical histories from patients, which can lead to errors or delays in diagnosis. Recent advances in artificial intelligence (AI) show promise in augmenting current human-driven methods of collecting personal and family histories; however, such tools are largely unproven. Objective: The main aim of this pilot study was to evaluate the feasibility and acceptability of a conversational AI medical interviewing system among patients. Methods: The study was conducted among adult patients empaneled at a family medicine clinic within a large academic medical center in Northern California. Participants were asked to test an AI medical interviewing system, which uses a conversational avatar and chatbot to capture medical histories and identify patients with risk factors. After completing an interview with the AI system, participants completed a web-based survey inquiring about the performance of the system, the ease of using the system, and attitudes toward the system. Responses on a 7-point Likert scale were collected and evaluated using descriptive statistics. Results: A total of 20 patients with a mean age of 50 years completed an interview with the AI system, including 12 females (60\%) and 8 males (40\%); 11 were White (55\%), 8 were Asian (40\%), and 1 was Black (5\%), and 19 had at least a bachelor's degree (95\%). Most participants agreed that using the system to collect histories could help their PCPs have a better understanding of their health (16/20, 80\%) and help them stay healthy through identification of their health risks (14/20, 70\%). Those who reported that the system was clear and understandable, and that they were able to learn it quickly, tended to be younger; those who reported that the tool could motivate them to share more comprehensive histories with their PCPs tended to be older. Conclusions: In this feasibility and acceptability pilot of a conversational AI medical interviewing system, the majority of patients believed that it could help clinicians better understand their health and identify health risks; however, patients were split on the effort required to use the system, and whether AI should be used for medical interviewing. Our findings suggest areas for further research, such as understanding the user interface factors that influence ease of use and adoption, and the reasons behind patients' attitudes toward AI-assisted history-taking. ",
doi="10.2196/37028",
url="https://formative.jmir.org/2022/6/e37028",
url="http://www.ncbi.nlm.nih.gov/pubmed/35759326"
}


@Article{info:doi/10.2196/32818,
author="Herrera, Tara
and Fiori, P. Kevin
and Archer-Dyer, Heather
and Lounsbury, W. David
and Wylie-Rosett, Judith",
title="Social Determinants of Health Screening by Preclinical Medical Students During the COVID-19 Pandemic: Service-Based Learning Case Study",
journal="JMIR Med Educ",
year="2022",
month="Jan",
day="17",
volume="8",
number="1",
pages="e32818",
keywords="social determinants of health",
keywords="service-based learning",
keywords="telehealth",
keywords="preclinical education",
keywords="screening",
keywords="referral",
keywords="community health workers",
keywords="determinant",
keywords="medical student",
keywords="case study",
keywords="service",
keywords="preparation",
keywords="pilot",
keywords="feasibility",
keywords="training",
keywords="assessment",
keywords="needs",
keywords="electronic health record",
keywords="questionnaire",
abstract="Background: The inclusion of social determinants of health is mandated for undergraduate medical education. However, little is known about how to prepare preclinical students for real-world screening and referrals for addressing social determinants of health. Objective: This pilot project's objective was to evaluate the feasibility of using a real-world, service-based learning approach for training preclinical students to assess social needs and make relevant referrals via the electronic medical record during the COVID-19 pandemic (May to June 2020). Methods: This project was designed to address an acute community service need and to teach preclinical, second-year medical student volunteers (n=11) how to assess social needs and make referrals by using the 10-item Social Determinants of Health Screening Questionnaire in the electronic health record (EHR; Epic platform; Epic Systems Corporation). Third-year medical student volunteers (n=3), who had completed 6 clinical rotations, led the 2-hour skills development orientation and were available for ongoing mentoring and peer support. All student-patient communication was conducted by telephone, and bilingual (English and Spanish) students called the patients who preferred to communicate in Spanish. We analyzed EHR data extracted from Epic to evaluate screening and data extracted from REDCap (Research Electronic Data Capture; Vanderbilt University) to evaluate community health workers' notes. We elicited feedback from the participating preclinical students to evaluate the future use of this community-based service learning approach in our preclinical curriculum. Results: The preclinical students completed 45 screening interviews. Of the 45 screened patients, 20 (44\%) screened positive for at least 1 social need. Almost all of these patients (19/20, 95\%) were referred to the community health worker. Half (8/16, 50\%) of the patients who had consultations with the community health worker were connected with a relevant social service resource. The preclinical students indicated that project participation increased their ability to assess social needs and make needed EHR referrals. Food insecurity was the most common social need. Conclusions: Practical exposure to social needs assessment has the potential to help preclinical medical students develop the ability to address social concerns prior to entering clinical clerkships in their third year of medical school. The students can also become familiar with the EHR prior to entering third-year clerkships. Physicians, who are aware of social needs and have the electronic medical record tools and staff resources needed to act, can create workflows to make social needs assessments and services integral components of health care. Research studies and quality improvement initiatives need to investigate how to integrate screening for social needs and connecting patients to the appropriate social services into routine primary care procedures. ",
doi="10.2196/32818",
url="https://mededu.jmir.org/2022/1/e32818",
url="http://www.ncbi.nlm.nih.gov/pubmed/35037885"
}


@Article{info:doi/10.2196/23599,
author="Hall, J. Natalie
and Berry, K. Sameer
and Aguilar, Jack
and Brier, Elizabeth
and Shah, Parth
and Cheng, Derek
and Herman, Jeremy
and Stein, Theodore
and Spiegel, R. Brennan M.
and Almario, V. Christopher",
title="Impact of an Online Gastrointestinal Symptom History Taker on Physician Documentation and Charting Time: Pragmatic Controlled Trial",
journal="JMIR Form Res",
year="2021",
month="May",
day="4",
volume="5",
number="5",
pages="e23599",
keywords="patient-provider portal",
keywords="computer-generated patient history",
keywords="patient-reported outcome",
keywords="gastrointestinal",
keywords="EHR",
abstract="Background: A potential benefit of electronic health records (EHRs) is that they could potentially save clinician time and improve documentation by auto-generating the history of present illness (HPI) in partnership with patients prior to the clinic visit. We developed an online patient portal called AEGIS (Automated Evaluation of Gastrointestinal [GI] Symptoms) that systematically collects patient GI symptom information and then transforms the data into a narrative HPI that is available for physicians to review in the EHR prior to seeing the patient. Objective: This study aimed to compare whether use of an online GI symptom history taker called AEGIS improves physician-centric outcomes vs usual care. Methods: We conducted a pragmatic controlled trial among adults aged ?18 years scheduled for a new patient visit at 4 GI clinics at an academic medical center. Patients who completed AEGIS were matched with controls in the intervention period who did not complete AEGIS as well as controls who underwent usual care in the pre-intervention period. Of note, the pre-intervention control group was formed as it was not subject to contamination bias, unlike for post-intervention controls. We then compared the following outcomes among groups: (1) documentation of alarm symptoms, (2) documentation of family history of GI malignancy, (3) number of follow-up visits in a 6-month period, (4) number of tests ordered in a 6-month period, and (5) charting time (difference between appointment time and time the encounter was closed). Multivariable regression models were used to adjust for potential confounding. Results: Of the 774 patients who were invited to complete AEGIS, 116 (15.0\%) finished it prior to their visit. The 116 AEGIS patients were then matched with 343 and 102 controls in the pre- and post-intervention periods, respectively. There were no statistically significant differences among the groups for documentation of alarm symptoms and GI cancer family history, number of follow-up visits and ordered tests, or charting time (all P>.05). Conclusions: Use of a validated online HPI-generation portal did not improve physician documentation or reduce workload. Given universal adoption of EHRs, further research examining how to optimally leverage patient portals for improving outcomes are needed. ",
doi="10.2196/23599",
url="https://formative.jmir.org/2021/5/e23599",
url="http://www.ncbi.nlm.nih.gov/pubmed/33944789"
}


@Article{info:doi/10.2196/25493,
author="Brandberg, Helge
and Sundberg, Johan Carl
and Spaak, Jonas
and Koch, Sabine
and Zakim, David
and Kahan, Thomas",
title="Use of Self-Reported Computerized Medical History Taking for Acute Chest Pain in the Emergency Department -- the Clinical Expert Operating System Chest Pain Danderyd Study (CLEOS-CPDS): Prospective Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="27",
volume="23",
number="4",
pages="e25493",
keywords="chest pain",
keywords="computerized history taking",
keywords="coronary artery disease",
keywords="eHealth",
keywords="emergency department",
keywords="health informatics",
keywords="medical history",
keywords="risk management",
abstract="Background: Chest pain is one of the most common chief complaints in emergency departments (EDs). Collecting an adequate medical history is challenging but essential in order to use recommended risk scores such as the HEART score (based on history, electrocardiogram, age, risk factors, and troponin). Self-reported computerized history taking (CHT) is a novel method to collect structured medical history data directly from the patient through a digital device. CHT is rarely used in clinical practice, and there is a lack of evidence for utility in an acute setting. Objective: This substudy of the Clinical Expert Operating System Chest Pain Danderyd Study (CLEOS-CPDS) aimed to evaluate whether patients with acute chest pain can interact effectively with CHT in the ED. Methods: Prospective cohort study on self-reported medical histories collected from acute chest pain patients using a CHT program on a tablet. Clinically stable patients aged 18 years and older with a chief complaint of chest pain, fluency in Swedish, and a nondiagnostic electrocardiogram or serum markers for acute coronary syndrome were eligible for inclusion. Patients unable to carry out an interview with CHT (eg, inadequate eyesight, confusion or agitation) were excluded. Effectiveness was assessed as the proportion of patients completing the interview and the time required in order to collect a medical history sufficient for cardiovascular risk stratification according to HEART score. Results: During 2017-2018, 500 participants were consecutively enrolled. The age and sex distribution (mean 54.3, SD 17.0 years; 213/500, 42.6\% women) was similar to that of the general chest pain population (mean 57.5, SD 19.2 years; 49.6\% women). Common reasons for noninclusion were language issues (182/1000, 18.2\%), fatigue (158/1000, 15.8\%), and inability to use a tablet (152/1000, 15.2\%). Sufficient data to calculate HEART score were collected in 70.4\% (352/500) of the patients. Key modules for chief complaint, cardiovascular history, and respiratory history were completed by 408 (81.6\%), 339 (67.8\%), and 291 (58.2\%) of the 500 participants, respectively, while 148 (29.6\%) completed the entire interview (in all 14 modules). Factors associated with completeness were age 18-69 years (all key modules: Ps<.001), male sex (cardiovascular: P=.04), active workers (all key modules: Ps<.005), not arriving by ambulance (chief complaint: P=.03; cardiovascular: P=.045), and ongoing chest pain (complete interview: P=.002). The median time to collect HEART score data was 23 (IQR 18-31) minutes and to complete an interview was 64 (IQR 53-77) minutes. The main reasons for discontinuing the interview prior to completion (n=352) were discharge from the ED (101, 28.7\%) and tiredness (95, 27.0\%). Conclusions: A majority of patients with acute chest pain can interact effectively with CHT on a tablet in the ED to provide sufficient data for risk stratification with a well-established risk score. The utility was somewhat lower in patients 70 years and older, in patients arriving by ambulance, and in patients without ongoing chest pain. Further studies are warranted to assess whether CHT can contribute to improved management and prognosis in this large patient group. Trial Registration: ClinicalTrials.gov NCT03439449; https://clinicaltrials.gov/ct2/show/NCT03439449 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-031871 ",
doi="10.2196/25493",
url="https://www.jmir.org/2021/4/e25493",
url="http://www.ncbi.nlm.nih.gov/pubmed/33904821"
}