Journal of Medical Internet Research

The leading peer-reviewed journal for digital medicine and health and health care in the internet age. 

Editor-in-Chief:

Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada


Impact Factor 7.4

The Journal of Medical Internet Research (JMIR) is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is a leading health services and digital health journal globally in terms of quality/visibility (Journal Impact Factor™ 7.4 (Clarivate, 2023)) and is also the largest journal in the field. The journal is ranked #1 on Google Scholar in the 'Medical Informatics' discipline. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth and informatics applications for patient education, prevention, population health and clinical care.

JMIR is indexed in all major literature indices including National Library of Medicine(NLM)/MEDLINE, Sherpa/Romeo, PubMed, PMCScopus, Psycinfo, Clarivate (which includes Web of Science (WoS)/ESCI/SCIE), EBSCO/EBSCO Essentials, DOAJ, GoOA and others. As a leading high-impact journal in its disciplines, ranking Q1 in both the 'Medical Informatics' and 'Health Care Sciences and Services' categories, it is a selective journal complemented by almost 30 specialty JMIR sister journals, which have a broader scope, and which together receive over 6.000 submissions a year. 

As an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews). Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to a different journal but can simply transfer it between journals. 

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

As all JMIR journals, the journal encourages Open Science principles and strongly encourages publication of a protocol before data collection. Authors who have published a protocol in JMIR Research Protocols get a discount of 20% on the Article Processing Fee when publishing a subsequent results paper in any JMIR journal.

Be a widely cited leader in the digital health revolution and submit your paper today!

Recent Articles

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New Methods

Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions.

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Ethics, Privacy, and Legal Issues

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.

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Chatbots and Conversational Agents

Parents experience many challenges during the perinatal period. Mobile app–based interventions and chatbots show promise in delivering health care support for parents during the perinatal period.

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Web-based and Mobile Health Interventions

Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics.

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Human Factors and Usability Case Studies

Interest in the application of predictive risk models (PRMs) in health care to identify people most likely to experience disease and treatment-related complications is increasing. In cancer care, these techniques are focused primarily on the prediction of survival or life-threatening toxicities (eg, febrile neutropenia). Fewer studies focus on the use of PRMs for symptoms or supportive care needs. The application of PRMs to chemotherapy-related symptoms (CRS) would enable earlier identification and initiation of prompt, personalized, and tailored interventions. While some PRMs exist for CRS, few were translated into clinical practice, and human factors associated with their use were not reported.

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Infodemiology and Infoveillance

Management and prevention of hypertension are important public health issues. Healthy dietary habits are one of the modifiable factors. As Twitter (subsequently rebranded X) is a digital platform that can influence public eating behavior, there is a knowledge gap regarding the information about foods and nutrients recommended for blood pressure control and who disseminates them on Twitter.

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Digital Health Reviews

Despite the well-documented health benefits associated with wearable monitoring devices (WMDs), adherence among community-dwelling older adults remains low. By providing guidance on the purpose and benefits of using WMDs, facilitating goal-setting aligned with the device’s features, promoting comprehension of the health data captured by the device, and assisting in overcoming technological challenges, peers and health care professionals can potentially enhance older adults’ adherence to WMDs. However, the effectiveness of such support mechanisms in promoting adherence to WMDs among older adults remains poorly understood.

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E-Health / Health Services Research and New Models of Care

Early warning scores (EWS) are routinely used in hospitals to assess a patient’s risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed.

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Robots in Healthcare

Technological advances in robotics, artificial intelligence, cognitive algorithms, and internet-based coaches have contributed to the development of devices capable of responding to some of the challenges resulting from demographic aging. Numerous studies have explored the use of robotic coaching solutions (RCSs) for supporting healthy behaviors in older adults and have shown their benefits regarding the quality of life and functional independence of older adults at home. However, the use of RCSs by individuals who are potentially vulnerable raises many ethical questions. Establishing an ethical framework to guide the development, use, and evaluation practices regarding RCSs for older adults seems highly pertinent.

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Infodemiology and Infoveillance

To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media’s potential remains largely untapped in real-world scenarios.

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Chatbots and Conversational Agents

Families of individuals with neurodevelopmental disabilities or differences (NDDs) often struggle to find reliable health information on the web. NDDs encompass various conditions affecting up to 14% of children in high-income countries, and most individuals present with complex phenotypes and related conditions. It is challenging for their families to develop literacy solely by searching information on the internet. While in-person coaching can enhance care, it is only available to a minority of those with NDDs. Chatbots, or computer programs that simulate conversation, have emerged in the commercial sector as useful tools for answering questions, but their use in health care remains limited. To address this challenge, the researchers developed a chatbot named CAMI (Coaching Assistant for Medical/Health Information) that can provide information about trusted resources covering core knowledge and services relevant to families of individuals with NDDs. The chatbot was developed, in collaboration with individuals with lived experience, to provide information about trusted resources covering core knowledge and services that may be of interest. The developers used the Django framework (Django Software Foundation) for the development and used a knowledge graph to depict the key entities in NDDs and their relationships to allow the chatbot to suggest web resources that may be related to the user queries. To identify NDD domain–specific entities from user input, a combination of standard sources (the Unified Medical Language System) and other entities were used which were identified by health professionals as well as collaborators. Although most entities were identified in the text, some were not captured in the system and therefore went undetected. Nonetheless, the chatbot was able to provide resources addressing most user queries related to NDDs. The researchers found that enriching the vocabulary with synonyms and lay language terms for specific subdomains enhanced entity detection. By using a data set of numerous individuals with NDDs, the researchers developed a knowledge graph that established meaningful connections between entities, allowing the chatbot to present related symptoms, diagnoses, and resources. To the researchers’ knowledge, CAMI is the first chatbot to provide resources related to NDDs. Our work highlighted the importance of engaging end users to supplement standard generic ontologies to named entities for language recognition. It also demonstrates that complex medical and health-related information can be integrated using knowledge graphs and leveraging existing large datasets. This has multiple implications: generalizability to other health domains as well as reducing the need for experts and optimizing their input while keeping health care professionals in the loop. The researchers' work also shows how health and computer science domains need to collaborate to achieve the granularity needed to make chatbots truly useful and impactful.

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Preprints Open for Peer-Review

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Open Peer Review Period:

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