Journal of Medical Internet Research

Prior microbiology results, resistance patterns, and antimicrobial exposure are central to safe and effective antimicrobial prescribing. Digital health fragmentation refers to the dispersal of patient data across multiple electronic systems and the associated challenge of accessing complete information at the point of care. Antimicrobial prescribing for infections represents a critical use case to investigate the impact of digital health fragmentation on patient care. While interoperability has been studied in the context of patient safety, no review has described digital health fragmentation within the United Kingdom and examined its impact on antimicrobial prescribing and antimicrobial stewardship (AMS).

Assessing medication adherence is central to quality care, yet linking electronic health record (EHR) medication orders to outpatient pharmacy dispense data remains technically complex.

Family caregivers of children with chronic health conditions experience substantial physical and mental health burdens, including burnout, anxiety, depression, fatigue, and sleep disturbances. Despite this need, validated digital mental health tools tailored to family caregivers remain limited. AI-powered conversational agents offer a promising approach for delivering on-demand, personalized mental health support, yet development and evaluation frameworks for this population are lacking.

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Cardiometabolic diseases (CMDs) are a major health concern worldwide, with people living in socioeconomically disadvantaged areas being disproportionately affected. Knowing one’s risk status for developing a CMD can help individuals in delaying or preventing the disease. Innovative tools and strategies such as the use of AI-based technology are needed to improve inclusivity, cost-effectiveness, and sustainability of screening processes.

AI is increasingly embedded in the institutions and environments that shape health. Yet current frameworks for understanding its implications for health equity remain underdeveloped. The social determinants of health tradition provides a strong foundation, and recent work on digital determinants of health has begun to address the health implications of digital transformation. However, AI warrants distinct conceptual attention because of its triple role: it operates simultaneously as a determinant of health in its own right, as a mediator and moderator of existing determinants, and as an amplifier of advantage and disadvantage over time. This viewpoint proposes a redistribution-translation-accumulation framework for analyzing how AI may contribute to the reproduction of health inequity. The framework comprises 2 analytically distinct mechanisms and 1 cross-cutting temporal dynamic. Redistribution captures how AI reshapes the distribution of health-relevant resources and opportunities, including education, employment, and income, while AI itself becomes an unequally distributed determinant. Translation describes how AI changes the pathways through which social positions are converted into health outcomes. Proxy-based decision rules can formalize historical inequities, diagnostic algorithms may perform unevenly across populations due to unrepresentative training data, and AI-mediated information environments can alter institutional responsiveness. Accumulation is conceptualized not as a third parallel mechanism but as a temporal amplifier operating on both mechanisms: AI-driven feedback loops and institutional embedding can concentrate advantage and disadvantage over time, often without users’ awareness. The framework is offered as a hypothesis-generating heuristic and is directionally neutral: under specifiable design, deployment, and governance conditions, the same mechanisms can narrow rather than widen health gaps in high-income and low- and middle-income settings alike. The framework has direct implications for governance. Current approaches such as the EU AI Act’s Fundamental Rights Impact Assessment (FRIA) and Canada’s Algorithmic Impact Assessment (AIA) advance AI accountability but assess systems largely before or at deployment and do not systematically track distributional health consequences. Building on this framework, I propose a distributional impact assessment as a complementary tool for equity-oriented AI governance. Structured around the 3 RTA dimensions, it asks whether an AI system alters the distribution of health-relevant resources across groups (redistribution), changes how social positions are converted into health (translation), and risks concentrating disadvantage over time through feedback and institutional embedding (accumulation). It is operationalized with candidate indicators, data sources, responsible actors, and reassessment triggers and is illustrated through a retrospective worked example of a biased care-management algorithm.

Thyroid fine-needle aspiration biopsy (FNAB) is a commonly used diagnostic procedure in patients with suspected thyroid cancer; however, it may induce pain, anxiety, and fear during the procedure.

Online health communities (OHCs) provide vital peer support and health information to individuals managing chronic conditions. However, sustained user engagement remains challenging, with many users reducing activity over time despite the ongoing benefits these platforms offer. While some individuals may improve or become more knowledgeable, sustained engagement remains critical because ongoing participation fosters trust, peer support, and continuous access to evolving health information that may persist beyond initial recovery or learning. Hence, it is important to consider how community needs evolve as users’ health needs and information-seeking behaviors change to inform how we support users through different stages of their health and participation journeys.

No measurement, no understanding; no understanding, no control: this foundational scientific principle was exposed as a public health dysfunction by the COVID-19 pandemic. Transmission chains spread invisibly, and the contact histories, mobility patterns, and biosignals necessary for control were never systematically collected. Although sensors and digital technologies existed, the fundamental reason measurement failed was the absence of privacy infrastructure that would have enabled people to provide data with confidence. This failure had structural reasons. The object of measurement in infectious disease control is not a physical phenomenon but human beings, and measurement therefore enters the core of privacy: contact histories, social relationships, and bodily states. Because greater precision also deepens privacy intrusion, contact-tracing apps faced 2 failures: privacy-centered designs lost epidemiological utility, while utility-centered designs were rejected through public distrust. Neither achieved sufficient measurement. This Viewpoint reframes the problem. Privacy protection is not a constraint that impedes infectious disease control but the enabling condition upon which effective measurement depends. Existing regulations and technical methods have not been designed from this premise and have therefore failed to break the cycle of structural distrust. As an institutional approach to filling this gap, we present VRAIO (verifiable record of AI output), which integrates democratic rule-setting, metadata declaration, third-party verification, tamper-proof ledgers, and violation-deterrence incentives. Once privacy infrastructure is established, this foundational principle can operate freely in infectious disease control for the first time. It will enable high-resolution epidemiology and precision intervention, opening a new path for public health that reconciles infection control with individual autonomy and social freedom without relying on blanket social shutdowns.

Self-directed digital mental health programs can increase access to evidence-based interventions for child anxiety. However, there has been little comprehensive investigation of real-world dissemination, especially using implementation science approaches.