Journal of Medical Internet Research

Eye tracking is recognized as a gold standard for measuring visual attention and cognitive engagement. In this study, it offers a useful lens for understanding how primary care providers balance patient communication with navigation of electronic health records (EHRs). We used wearable eye tracking to collect visual information processing behavior and conducted a retrospective think-aloud protocol to examine how primary care clinicians processed suiciderelated information (CAT-MH(R)) embedded in...

BackgroundTyping in the electronic health record (EHR) takes up healthcare providers time and cognitive space and constitutes a substantial administrative burden contributing to high burnout rates in healthcare. Ambient digital scribes may improve this problem. ObjectiveTo investigate the effect of the use of Autoscriber, an ambient digital scribe, on healthcare providers administrative workload and the quality of medical notes in the EHR. MethodsA study period of 26 weeks was randomized into ...

Digital therapeutics (DTx) are patient-facing apps designed to support individuals in their daily lives. Therefore, they have the potential to revolutionize healthcare by empowering and engaging patients to become active players in their own care. Despite the increasing adoption of DTx in national healthcare systems, research on their design remains limited. The present study introduces "DiGATax", a taxonomy designed to categorize and analyze DTx, including perspectives on content, intervention ...

ObjectiveMore people than ever before are living with cancer. Patient education is a core component of cancer care, and patients are increasingly using large language models (LLMs), such as ChatGPT, for advice. The objectives of this study were to evaluate the ability of ChatGPT to explain specialist cancer care records (multidisciplinary team (MDT) meeting reports) to patients and to understand key stakeholders views and opinions about the technology. MethodsSix simulated MDT meeting reports w...

In 2021, the U.S. Surgeon General issued an advisory to address the youth mental health crisis that included actions media organizations could take regarding their coverage of mental illness and traumatic events including natural disasters, pandemics, and mass violence. Although research indicates that both news and social media are associated with worse adolescent mental health outcomes, it is unknown whether news outlets adhere to the U.S. Surgeon Generals recommendations on social media. This...

General-purpose large language models (LLMs) like ChatGPT are increasingly used for medical advice despite lacking medical training and frequently producing incorrect or unsafe output. Older adults health information-seeking behaviors using LLMs remain poorly characterized. We conducted a cross-sectional survey of 574 US adults aged 50+ recruited via Prolific, balanced by sex and race. Participants reported health information sources, ChatGPT and PubMed use, demographics, and health literacy. Mo...

Health information seeking has fundamentally changed since the onset of Large Language Models (LLM), with nearly one third of ChatGPTs 800 million users asking health questions weekly. Understanding the sources of those AI generated responses is vital, as health organizations and providers are also investing in digital strategies to organically improve their ranking, reach and visibility in LLM systems like ChatGPT. As AI search optimization strategies are gaining maturity, this study introduces...

Rare diseases affect millions worldwide and are associated with long diagnostic delays, limited access to treatments, and substantial challenges in daily care and coordination. Digital health technologies, including mobile apps, telehealth, and data-sharing platforms, offer opportunities to improve care and quality of life for people living with rare diseases. As these tools rapidly expand, this study examines the needs, expectations, and conditions for successful adoption of patient-centered di...

This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. Unlike traditional multi-class approaches that often struggle with comorbidity, the proposed method employs a binary relevance strategy using fine-tuned DistilBERT models to identify co-occurring conditions, including depression, anxiety, and narcissistic personality disorder. To address class imbalance and optimize decision boundaries, the framewor...

This paper describes eHEALS.com.br, a web-based platform that automates the administration of the Brazilian eHealth Literacy Scale (eHEALS-Br). The system collects responses online, scores users in real time, and provides personalized feedback based on five levels of digital health literacy. A systematic literature review was conducted to map existing instruments and identify gaps related to automation, temporal control, and inclusion. The platform architecture combines a React and TypeScript fr...

Artificial intelligence (AI) is dramatically changing the healthcare landscape by providing patients, clinicians, administrators, and public health professionals with tools aiming to improve efficiency, outcomes, and experience in health. As elsewhere, New York State (NYS) experiences high demand for - and high investment in - transformation in healthcare with AI tools, though little is known about clinicians use and interest in adopting AI tools in their work. A large share of the nations futur...

The emergence of Janus kinase (JAK) inhibitors, a relatively new class of medications for autoimmune and inflammatory conditions, has been accompanied by reports of adverse effects observed during clinical trials. However, uncertainty over their safety and efficacy in wider, unselected populations has led to discussion and speculation on social media such as Reddit. Social networks represent a novel, rich source of real-world pharmacovigilance data. They are also an environment where unverified ...

BackgroundArtificial intelligence is increasingly embedded in healthcare delivery. Its legitimacy depends on institutional governance, not technical performance alone. Prior research has centered on clinicians and patients. Less attention has been given to cybersecurity professionals who sustain the digital infrastructures that support health AI. This study examines how cybersecurity professionals conceptualize AI as clinical infrastructure and how these interpretations shape understandings of t...

ObjectiveTo characterize the clinical and administrative concerns communicated through secure ophthalmology messaging and to assess differences in message content across patient sociodemographic groups. DesignCross-sectional study of de-identified, patient-initiated secure messages sent between June 2014 and July 2024. ParticipantsPatients with ophthalmic conditions who initiated secure electronic health record portal messages. Of 48 516 extracted message threads, 30 390 patient medical advice...

BackgroundCo-occurring substance use and mental health disorders (COD) represent a growing public health concern, yet healthcare utilization studies with a large sample size remain limited. This study examined healthcare utilization patterns and sociodemographic correlates among COD adults using data from the All of Us Research Program (2018-2023). MethodsElectronic health record data were analyzed for adults aged [≥]18 years with confirmed diagnoses of substance use and mental health disord...

BackgroundGenerative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language models (LLMs) can generate draft responses to patient messages using electronic health record (EHR) data. This could mitigate increased workload related to high message volumes. While effectiveness and feasibility of these GenAI tools have been studied in the United States, evidence from non-English contexts is scarce, particularly regarding user experie...

BackgroundTinnitus affects a substantial proportion of the global population and can severely disrupt sleep, mood, and daily functioning, yet the quality of mobile health apps designed for tinnitus management remains highly variable. Traditional evaluation methods, including clinical trials, expert rating scales, and small-scale surveys, rarely capture large-scale, feature-level feedback from real-world users, leaving a gap in understanding which app characteristics drive sustained engagement an...

ImportanceClinician adoption and adaptation of new tools evolve over time. Prior studies of ambient Artificial intelligence (AI) scribes have primarily relied on single time-point measurements (e.g., pre-post), potentially obfuscating their true impact on outcomes. ObjectiveTo investigate longitudinal effects of an AI scribe tool on patient encounter-level outcomes. DesignCase series across 48 weeks (24 pre, 24 post) per clinician. SettingPrimary care clinical encounters occurring between 01/...

Technological innovations such as eHealth are vital for improving healthcare accessibility, quality, and sustainability. While most research addresses adoption at the individual or team level, less is known about organisational factors enabling sustainable transformation. Organisational readiness is a key determinant of success. The Organizational eHealth Readiness (OeHR) model, developed in Polish primary care, assesses five dimensions: Strategy, Competence, Culture, Structure, and Technology, ...

ObjectiveAmbient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. Materials and MethodsEight coders analyzed clinical documentation generated by ambient AI from 20...