Journal of Medical Internet Research

Chronic musculoskeletal pain is a major public health problem, and access to continuous, long-term care remains challenging. Digital health interventions may extend care beyond conventional settings; however, their comparative effects across delivery modalities and core therapeutic components remain uncertain.

Digital health increasingly depends on data exchange across institutions, technologies, and jurisdictions, creating persistent challenges for the governance of access, consent, interoperability, provenance, and accountability. Blockchain and distributed ledger technology (DLT) systems have been proposed as mechanisms for coordinating and verifying governance processes across distributed actors. However, existing research has examined individual applications or technical domains, leaving unclear how blockchain/DLT systems function as governance infrastructure across health care, and whether these systems have progressed toward real-world implementation.

National surveillance of commercial retail environments remains limited by data sources that are updated infrequently and capture narrow dimensions of food access. The Google Places API provides continuously updated and programmatically accessible information on business locations across the United States, but its use as a population-level built-environment exposure measure has not been systematically evaluated.

Retinopathy of Prematurity (ROP) is a leading cause of preventable childhood blindness; yet, a global shortage of experienced pediatric ophthalmologists impedes timely diagnosis and treatment. While emerging AI chatbots are promising clinical decision-support tools in some ophthalmic diseases, their performance in ROP diagnosis and providing treatment suggestions remains uncertain.

Medication nonadherence after percutaneous coronary intervention (PCI) remains a major barrier to secondary prevention. Prior SMS text messaging interventions have shown inconsistent results, often limited to reminders without addressing behavioral or psychological determinants.

Large language model (LLM)–based systems have tremendous potential to improve patient-centered health care, especially for medically underserved populations. However, realizing this potential requires careful consideration of the socio-technical contexts in which LLMs are used. The design of such systems should consider the vulnerabilities of any medically underserved group that the system intends to support, and provide trustworthy evidence for its use. This viewpoint reports the lessons learned from our experience and research with the CREATE (Culture, Respect, Education, Advancement, Trust, and Expertise) framework for engaging multiple stakeholders to guide the integration of LLM-based systems for improved health care delivery. We propose an extension of the traditional hierarchy of evidence model and identify key junction points in clinical workflows at which LLM-based systems can potentially be used to improve patient care. The key messages can be summarized as follows: (1) users of AI technologies, such as LLM-based tools, must be aware of the strengths, limitations, and impact of these technologies on health care delivery workflows and on the quality of generated evidence on which health care recommendations are based; (2) users must also be aware of the impact of data quality on AI outputs and on the evidence generated from the use of these systems; (3) the evidence pyramid provides a framework that facilitates the evaluation of the output generated by AI systems; (4) the CREATE framework facilitates the engagement of a variety of stakeholders. We propose concrete approaches for its validation and implementation; (5) any system designed to support people with opioid use disorder (OUD) needs to consider the overall lack of trust and stigmatizing experiences of this population with the health care system, and we discuss various aspects of the evaluation process necessary to build trust; (6) to discuss research directions that need to be addressed before LLM-based systems can be integrated usefully in patient health care.

Risk disclosure before bronchoscopy should provide sufficient information for informed consent, but detailed text-based risk disclosure may increase procedural anxiety. Patient-specific visualization with a digital twin–based bronchoscopy simulator may help patients understand bronchoscopy and its risks in a more individualized manner.

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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