JMIR Medical Informatics

Electronic health records offer vast clinical data for health care research, but interoperability challenges often hinder comprehensive analysis. The Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard addresses these challenges, although its nested and interconnected resource format can be complex for analytics. Several tools have emerged to facilitate analytical access, either by querying FHIR servers via representational state transfer (REST) APIs or encoding resources in relational formats. However, the performance implications of these methods remain largely unexplored.

Effective risk stratification in sepsis remains a critical clinical challenge. Serum lactate is a cornerstone biomarker of metabolic dysfunction, yet its predictive limitations—particularly in patients without severe hyperlactatemia—are well recognized. The lactate-to-albumin ratio (LAR), a composite mixed-unit index integrating markers of acute metabolic dysfunction and systemic inflammation, has emerged as a promising predictor; however, its incremental discriminative advantage over lactate had not been formally tested in a large multicenter cohort using paired statistical methodology.

Enhancing the effectiveness of human-AI collaborative consultation in online health communities (OHCs) constitutes a core requirement for optimizing the allocation of medical resources and promoting the sustainable development of medical services. Nevertheless, the pathways to improving such effectiveness remain insufficiently understood.

Population health management requires tools that transform complex clinical data into actionable insights to guide care coordination, community outreach, and system-level planning.

The Swiss Personalized Health Network (SPHN) facilitates the interoperability and secure sharing of health-related data for research in Switzerland, in line with the findable, accessible, interoperable, and reusable (FAIR) principles. Since medical datasets can be highly sensitive, access is often governed by complex legal and regulatory requirements. Enabling researchers to discover, understand, and evaluate datasets through rich, well-structured metadata is therefore essential to support informed decisions about data suitability and reuse.

Health checkup programs in China reach millions of older adults for cardiovascular risk screening, but few effective mechanisms exist to ensure high-risk individuals receive follow-up preventive care.

The Delphi method is widely used to derive expert consensus on complex clinical problems, yet it is slow and resource intensive. Recent advances in large language models and retrieval‑augmented generation (RAG) offer the possibility of accelerating consensus while maintaining methodological rigor. Large language models can retrieve and summarize evidence, but they frequently hallucinate and cannot reliably cite sources. At the same time, RNA‑based drugs and messenger RNA vaccines are rapidly moving from concept to clinic, generating a pressing need for timely, evidence‑based consensus on regulatory, manufacturing, and clinical issues.

Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. Artificial intelligence (AI) medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous, and data from European health systems are especially scarce. This evaluation combines 2 complementary data sources: objective editing metadata from 236,153 notes generated by 1295 clinicians, describing operational editing behavior within the AI medical scribe, and paired self-reported survey responses from 177 fully onboarded clinicians, capturing perceived change in documentation time and clinician experience.

Psychiatric clinical notes in electronic health records (EHRs) provide rich longitudinal information that can support clinical decision-making. Using historical medical data can enable earlier identification of mental illness, better characterization of disease trajectories, and more personalized treatment planning. Natural language processing (NLP) transforms these unstructured notes into analyzable representations for research and care.

Primary care is becoming increasingly complex, with primary care physicians (PCPs) facing rising workloads driven by workforce shortages, growing administrative demands, and expanding clinical responsibilities. Recent advances in large language models (LLMs) offer new opportunities to support PCPs across clinical, administrative, and communication-related tasks within their workflows. Understanding how these technologies are perceived and used in primary care practice is, therefore, critical to inform their safe, effective, and human-centered implementation.

Cardiovascular diseases (CVDs) and type 2 diabetes (DM2) are influenced not only by biomedical risk factors but also by social determinants of health (SDOH). While the inclusion of SDOH in predictive models is increasingly advocated, few studies have quantified their specific contribution in a high-risk clinical cohort using robust statistical and machine-learning approaches.

Supraspinatus tendon pathologies are common causes of shoulder pain. Magnetic resonance imaging (MRI) is the reference imaging method but requires expert interpretation. Automated classification may improve diagnostic consistency and support musculoskeletal imaging workflows.