JMIR Medical Informatics

Emergency departments (EDs) are vital access points for urgent care, yet persistent overcrowding in health care systems worldwide compromises service quality and efficiency. Urban tertiary care centers are increasingly strained by surging patient numbers and scarce inpatient beds. Prior admission prediction studies mostly rely on limited triage features and discrimination metrics, neglecting operational factors, calibration, and explainability. Identifying likely admissions early could enable proactive resource allocation and shorter wait times.

Clinical documentation is a major contributor to physician workload and burnout, particularly in high-acuity settings such as emergency departments. Ambient AI scribes have emerged as a response, but commercial products cost US $99 to more than US $600 per physician per month, operate as opaque systems, and often process data on vendor-controlled infrastructure. These characteristics limit organizational control over data sovereignty, quality improvement, and clinical workflows and create barriers to adoption in resource-constrained settings.

Demand for health data to support research, innovation, and evidence-based policymaking is increasing, but access to patient-level data is constrained by privacy and data protection requirements. To address this challenge, the Czech Republic developed a national synthetic data infrastructure within the National Health Information System (NHIS). The infrastructure aims to support privacy-preserving secondary use of health data by enabling the development and validation of analytical workflows without providing direct access to operational patient-level databases.

Heart failure develops through complex longitudinal interactions among multiple chronic diseases, making longitudinal disease trajectories an important source of clinical information. Longitudinal disease trajectories contain rich structural information that reflects disease evolution and comorbidity interactions. However, this information is often lost when electronic health record (EHR) data are summarized using conventional pooling strategies or sparse International Classification of Diseases (ICD) representations.

Large language models (LLMs) are increasingly used for health information, but chronic obstructive pulmonary disease (COPD)–related questions often involve safety-sensitive decisions, including treatment escalation, oxygen therapy, and exacerbation management. Because generative outputs may vary across repeated prompts, response stability is also relevant to comparative evaluation.

Pharmacoepidemiologic studies using real-world data often lack clinical context embedded in free-text electronic health records (EHRs). However, sharing EHR narratives is restricted by privacy requirements, and conventional deidentification approaches may reduce analytic utility and limit auditability.

The integration of AI-based Clinical Decision Support Systems (AI-CDSS) into clinical practice has expanded rapidly, particularly in medical imaging. Beyond algorithmic performance, growing evidence suggests that clinicians’ engagement with AI-CDSS is shaped by how transparently systems communicate their outputs, and how trust is formed through human-AI interaction. However, a limited understanding of how these concepts are perceived by end users and developers continues to hinder real-world implementation.

Electronic medical record (EMR) certification programs have traditionally focused on product-level requirements, such as interoperability, security, and core functionality. However, growing evidence suggests that many patient safety risks associated with EMRs arise after implementation and are closely related to system configuration, workflow integration, and real-world use. Despite the presence of a national EMR product certification system in Korea, a systematic evaluation of how certified systems are implemented and used in clinical practice is still lacking.

The administrative burden of manual documentation has been a primary driver of physician burnout and a barrier to patient-centered care. Ambient scribes have emerged as a promising solution that uses passive audio capture and natural language processing to automate clinical note-taking. While their impact on clinical efficiency is frequently studied, there is a lack of research on how these tools have impacted interpersonal communication during clinical encounters.

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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.