Journal of Medical Internet Research

Patient-accessible electronic health records (PAEHRs) aim to enhance patient engagement by providing access to clinical information. In Sweden, implementation of a national PAEHR began in 2012 and reached nationwide scale-up in 2018. Adoption and use have steadily increased over time. So far, few studies report on how perspectives on PAEHRs evolve, informing on long-term use and development of the services.

Insomnia is common, and treatments are limited by tolerability and access. Digital health interventions (DHIs) are increasingly used for sleep management, but evidence comparing multiple outcomes is scarce.

Algorithm-enabled remote monitoring using cardiac implantable electronic devices (CIEDs) is increasingly used to support earlier detection of clinical deterioration in heart failure and in at-risk populations. However, the economic value of these technologies remains uncertain because intervention design, implementation, and evaluation methods vary.

This viewpoint addresses developers and evaluators of large language model (LLM)–based clinical decision support systems. Although recent benchmarks increasingly evaluate multiple clinical reasoning domains, many still rely substantially on reference decision agreement—whether model outputs conform to predefined decisions. Our aim is to clarify when such agreement remains useful, when it becomes insufficient for complex or preference-sensitive decisions, and how it can be complemented by reasoning-centered assessment. Clinical decision-making often depends on structured reasoning under uncertainty, integrating probabilistic evidence, contextual constraints, and patient values. Using oncology as an illustrative domain, we argue that some decision variability is a structural feature of clinical reasoning rather than noise or error. Used alone in such settings, single-reference benchmarks may conflate reasonable divergence with incorrectness and reward answer imitation over transparent reasoning. We conclude that reference decision agreement should be retained for tasks with stable ground truth but supplemented by evidence grounding, reasoning quality, patient-centeredness, and safety checks for open-ended clinical decision support. This multidimensional approach is intended to support safer and more clinically meaningful evaluation.

DeepSeek, an open-source large language model (LLM) extensively adopted in hospitals across China, presents a locally deployable alternative to proprietary models such as GPT. However, its real-world clinical performance and safeguards remain underexplored.

Gamified digital mental health interventions have been proposed as a scalable and engaging approach to addressing the substantial treatment gap in child and adolescent mental health, including limited access to evidence-based care and difficulties with treatment engagement. However, the evidence remains fragmented, and it is unclear which specific gamification elements contribute to clinical effectiveness and sustained engagement.

Conversational agents (CAs) are increasingly used to deliver scalable interventions for depression and anxiety, but existing evidence has often treated these tools as a single category. The comparative effectiveness of different agent types, intervention doses, and levels of human support remains unclear.

Health systems increasingly screen patients for social and whole-person needs. Yet, electronic health record data often remain passive when no workflow translates identified needs into structured action during routine care. In safety-net and federally qualified health center (FQHC) settings, this gap is especially consequential because clinicians must address multidomain needs within time-constrained encounters while meeting documentation, coding, and referral requirements.

Large language models (LLMs) are increasingly used in academic writing and can generate linguistically polished but factually incorrect scientific content. Such misinformation may distort evidence appraisal and, if translated into clinical practice, compromise patient care.

Given that randomized controlled trials (RCTs) are the gold standard for effectiveness research, assessing their risk of bias (RoB) becomes an indispensable step in systematic reviews. Yet this task remains time-consuming and prone to reviewer subjectivity. While large language models may accelerate the process, they lack transparent, traceable judgments linked to trial reports.