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 5.8 CiteScore 14.4
Recent Articles
![Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis Article Thumbnail](https://asset.jmir.pub/assets/ac730e9c2ac88d97a260b6b6ef360777.png 480w,https://asset.jmir.pub/assets/ac730e9c2ac88d97a260b6b6ef360777.png 960w,https://asset.jmir.pub/assets/ac730e9c2ac88d97a260b6b6ef360777.png 1920w,https://asset.jmir.pub/assets/ac730e9c2ac88d97a260b6b6ef360777.png 2500w)
Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.
![Resilient Artificial Intelligence in Health: Synthesis and Research Agenda Toward Next-Generation Trustworthy Clinical Decision Support Article Thumbnail](https://asset.jmir.pub/assets/8c1c9e4cec0276458b833c4426afe3d8.png 480w,https://asset.jmir.pub/assets/8c1c9e4cec0276458b833c4426afe3d8.png 960w,https://asset.jmir.pub/assets/8c1c9e4cec0276458b833c4426afe3d8.png 1920w,https://asset.jmir.pub/assets/8c1c9e4cec0276458b833c4426afe3d8.png 2500w)
Artificial intelligence (AI)–based clinical decision support systems are gaining momentum by relying on a greater volume and variety of secondary use data. However, the uncertainty, variability, and biases in real-world data environments still pose significant challenges to the development of health AI, its routine clinical use, and its regulatory frameworks. Health AI should be resilient against real-world environments throughout its lifecycle, including the training and prediction phases and maintenance during production, and health AI regulations should evolve accordingly. Data quality issues, variability over time or across sites, information uncertainty, human-computer interaction, and fundamental rights assurance are among the most relevant challenges. If health AI is not designed resiliently with regard to these real-world data effects, potentially biased data-driven medical decisions can risk the safety and fundamental rights of millions of people. In this viewpoint, we review the challenges, requirements, and methods for resilient AI in health and provide a research framework to improve the trustworthiness of next-generation AI-based clinical decision support.
![Patient Portals Fail to Collect Structured Information About Who Else is Involved in a Person’s Care Article Thumbnail](https://asset.jmir.pub/assets/d976862e5c0215086ad8babdd4b476d6.png 480w,https://asset.jmir.pub/assets/d976862e5c0215086ad8babdd4b476d6.png 960w,https://asset.jmir.pub/assets/d976862e5c0215086ad8babdd4b476d6.png 1920w,https://asset.jmir.pub/assets/d976862e5c0215086ad8babdd4b476d6.png 2500w)
The US health care delivery system does not systematically engage or support family or friend care partners. Meanwhile, the uptake and familiarity of portals to personal health information are increasing among patients. Technology innovations, such as shared access to the portal, use separate identity credentials to differentiate between patients and care partners. Although not well-known, or commonly used, shared access allows patients to identify who they do and do not want to be involved in their care. However, the processes for patients to grant shared access to portals are often limited or so onerous that interested patients and care partners often circumvent the process entirely. As a result, the vast majority of care partners resort to accessing portals using a patient’s identity credentials—a “do-it-yourself” solution in conflict with a health systems’ legal responsibility to protect patient privacy and autonomy. The personal narratives in this viewpoint (shared by permission) elaborate on quantitative studies and provide first-person snapshots of challenges faced by patients and families as they attempt to gain or grant shared access during crucial moments in their lives. As digital modalities increase patient roles in health care interactions, so does the importance of making shared access work for all stakeholders involved—patients, clinicians, and care partners. Electronic health record vendors must recognize that both patients and care partners are important users of their products, and health care organizations must acknowledge and support the critical contributions of care partners as distinct from patients.
![A Nordic Perspective on Patient Online Record Access and the European Health Data Space Article Thumbnail](https://asset.jmir.pub/assets/8712b179e4f2f446a0039b2f6500af10.png 480w,https://asset.jmir.pub/assets/8712b179e4f2f446a0039b2f6500af10.png 960w,https://asset.jmir.pub/assets/8712b179e4f2f446a0039b2f6500af10.png 1920w,https://asset.jmir.pub/assets/8712b179e4f2f446a0039b2f6500af10.png 2500w)
The Nordic countries are, together with the United States, forerunners in online record access (ORA), which has now become widespread. The importance of accessible and structured health data has also been highlighted by policy makers internationally. To ensure the full realization of ORA’s potential in the short and long term, there is a pressing need to study ORA from a cross-disciplinary, clinical, humanistic, and social sciences perspective that looks beyond strictly technical aspects. In this viewpoint paper, we explore the policy changes in the European Health Data Space (EHDS) proposal to advance ORA across the European Union, informed by our research in a Nordic-led project that carries out the first of its kind, large-scale international investigation of patients’ ORA—NORDeHEALTH (Nordic eHealth for Patients: Benchmarking and Developing for the Future). We argue that the EHDS proposal will pave the way for patients to access and control third-party access to their electronic health records. In our analysis of the proposal, we have identified five key principles for ORA: (1) the right to access, (2) proxy access, (3) patient input of their own data, (4) error and omission rectification, and (5) access control. ORA implementation today is fragmented throughout Europe, and the EHDS proposal aims to ensure all European citizens have equal online access to their health data. However, we argue that in order to implement the EHDS, we need more research evidence on the key ORA principles we have identified in our analysis. Results from the NORDeHEALTH project provide some of that evidence, but we have also identified important knowledge gaps that still need further exploration.
![A Symptom-Checker for Adult Patients Visiting an Interdisciplinary Emergency Care Center and the Safety of Patient Self-Triage: Real-Life Prospective Evaluation Article Thumbnail](https://asset.jmir.pub/assets/d91bfce7ba3e1334e9dc61ea6e1c260a.png 480w,https://asset.jmir.pub/assets/d91bfce7ba3e1334e9dc61ea6e1c260a.png 960w,https://asset.jmir.pub/assets/d91bfce7ba3e1334e9dc61ea6e1c260a.png 1920w,https://asset.jmir.pub/assets/d91bfce7ba3e1334e9dc61ea6e1c260a.png 2500w)
Symptom-checkers have become important tools for self-triage, assisting patients to determine the urgency of medical care. To be safe and effective, these tools must be validated, particularly to avoid potentially hazardous undertriage without leading to inefficient overtriage. Only limited safety data from studies including small sample sizes have been available so far.
![Assessing the Reproducibility of the Structured Abstracts Generated by ChatGPT and Bard Compared to Human-Written Abstracts in the Field of Spine Surgery: Comparative Analysis Article Thumbnail](https://asset.jmir.pub/assets/9c39ab331c912b82b9ca7745d85606c0.png 480w,https://asset.jmir.pub/assets/9c39ab331c912b82b9ca7745d85606c0.png 960w,https://asset.jmir.pub/assets/9c39ab331c912b82b9ca7745d85606c0.png 1920w,https://asset.jmir.pub/assets/9c39ab331c912b82b9ca7745d85606c0.png 2500w)
Due to recent advances in artificial intelligence (AI), language model applications can generate logical text output that is difficult to distinguish from human writing. ChatGPT (OpenAI) and Bard (subsequently rebranded as “Gemini”; Google AI) were developed using distinct approaches, but little has been studied about the difference in their capability to generate the abstract. The use of AI to write scientific abstracts in the field of spine surgery is the center of much debate and controversy.
![Antibiotic Prescribing by Digital Health Care Providers as Compared to Traditional Primary Health Care Providers: Cohort Study Using Register Data Article Thumbnail](https://asset.jmir.pub/assets/25a942dfec41f964aa34bb1a8806f248.png 480w,https://asset.jmir.pub/assets/25a942dfec41f964aa34bb1a8806f248.png 960w,https://asset.jmir.pub/assets/25a942dfec41f964aa34bb1a8806f248.png 1920w,https://asset.jmir.pub/assets/25a942dfec41f964aa34bb1a8806f248.png 2500w)
“Direct-to-consumer (DTC) telemedicine” is increasing worldwide and changing the map of primary health care (PHC). Virtual care has increased in the last decade and with the ongoing COVID-19 pandemic, patients’ use of online care has increased even further. In Sweden, online consultations are a part of government-supported health care today, and there are several digital care providers on the Swedish market, which makes it possible to get in touch with a doctor within a few minutes. The fast expansion of this market has raised questions about the quality of primary care provided only in an online setting without any physical appointments. Antibiotic prescribing is a common treatment in PHC.
![Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses Article Thumbnail](https://asset.jmir.pub/assets/5f112c35cff74c611c5931869a332823.png 480w,https://asset.jmir.pub/assets/5f112c35cff74c611c5931869a332823.png 960w,https://asset.jmir.pub/assets/5f112c35cff74c611c5931869a332823.png 1920w,https://asset.jmir.pub/assets/5f112c35cff74c611c5931869a332823.png 2500w)
Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses.
![Effectiveness of Web-Based Mindfulness-Based Interventions for Patients With Cancer: Systematic Review and Meta-Analyses Article Thumbnail](https://asset.jmir.pub/assets/d31d4ed01046dd6c7930bf3db2701179.png 480w,https://asset.jmir.pub/assets/d31d4ed01046dd6c7930bf3db2701179.png 960w,https://asset.jmir.pub/assets/d31d4ed01046dd6c7930bf3db2701179.png 1920w,https://asset.jmir.pub/assets/d31d4ed01046dd6c7930bf3db2701179.png 2500w)
Cancer has emerged as a considerable global health concern, contributing substantially to both morbidity and mortality. Recognizing the urgent need to enhance the overall well-being and quality of life (QOL) of cancer patients, a growing number of researchers have started using online mindfulness-based interventions (MBIs) in oncology. However, the effectiveness and optimal implementation methods of these interventions remain unknown.
![Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review Article Thumbnail](https://asset.jmir.pub/assets/c03b2e4bc32970fc237da68a683aca17.png 480w,https://asset.jmir.pub/assets/c03b2e4bc32970fc237da68a683aca17.png 960w,https://asset.jmir.pub/assets/c03b2e4bc32970fc237da68a683aca17.png 1920w,https://asset.jmir.pub/assets/c03b2e4bc32970fc237da68a683aca17.png 2500w)
The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers.
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