JMIR Medical Informatics

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AI scribing tools to assist health care providers by generating clinical notes are rapidly spreading across health care settings. Although these tools may reduce documentation burden, successful implementation depends on the contextual factors influencing their integration into clinical practice. Implementation evaluations are needed to inform practice integration, guide future adoption, and enhance effective use of this novel technology.

Digital decision-support tools such as triage systems and symptom checkers support millions of health-related decisions each year. Their quality and safety are commonly evaluated using textual patient cases, known as case vignettes. However, expert-written vignette sets cover only a limited spectrum of real-world patient presentations and lack population weights, which would allow extrapolating evaluation results to the underlying patient population.

Machine learning has been extensively applied in medical prediction; however, existing machine-assisted pharmacotherapy review systems predominantly rely on structured, codifiable data while neglecting unstructured natural language clinical narratives, thereby allowing latent medication risks to remain undetected.

Continuous patient monitoring generates vast volumes of high-frequency monitoring data, such as electrocardiogram, electroencephalography, and invasive blood pressure tracings, that are often persisted in heterogeneous, noninteroperable formats, limiting integration with electronic health records and secondary use. Although Fast Healthcare Interoperability Resources (FHIR) supports general health data exchange, native support for high-frequency medical monitoring data, including associated metadata and data quality metrics, remains limited.

Kidney Disease: Improving Global Outcomes (KDIGO) classifies chronic kidney disease (CKD) using categories based on both glomerular filtration rate and albuminuria. Structured measures remain essential but may not capture contextual clinical concerns documented in narrative records.

The European Health Dataspace (EHDS) expands health data sharing beyond direct care and introduces governance, transparency, trust, and secondary-use considerations that are not fully captured by established technology acceptance models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). To address this gap, we developed the health dataspace technology acceptance model (HDS-TAM) and evaluated it using the German electronic patient record (ePA) as an illustrative health dataspace (HDS) application.

Rare genetic diseases often present with recognizable facial phenotypes. Whether multimodal large language models (MLLMs) can prioritize the diagnosis of rare genetic diseases from facial images and whether image enhancement or visual prompting can improve this process remain unclear.

Accurate diagnosis of middle ear diseases remains challenging due to the specialized nature of otoscopic interpretation and the global shortage of otolaryngologists.

Many digital health and AI initiatives show promising results in pilot settings but do not progress to sustainable services. Pilots can operate within a predefined collaboration, whereas operational services must accommodate changes in participating organizations, available resources, and analytical capabilities while preserving established governance and technical arrangements.

Electronic health record–derived process measures such as vital-sign record density are used for quality monitoring, risk prediction, and cross-system benchmarking, but whether they retain consistent meaning across sites and data streams is rarely tested.

Frailty is prevalent among hospitalized patients and is associated with increased risk of complications, prolonged hospitalization, higher mortality, and health care costs. Existing frailty screening tools, including the Clinical Frailty Scale (CFS) and Hospital Frailty Risk Score (HFRS), have limitations related to subjectivity, resource intensity, or reliance on retrospective data, limiting their use in routine acute care. Early identification of frailty is critical to support targeted interventions and resource allocation, yet scalable approaches for predicting frailty early during hospital admission remain limited.