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Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated measures (eg, total time). We investigated how audit logs could be modeled using large language model (LLM) architectures to learn underlying workflow sequences during clinician-EHR interactions.

The objective of this study was to construct a harmonized SNOMED Clinical Terms (SNOMED CT) components quality assurance framework for identifying, categorizing, and assessing data quality issues in SNOMED CT components.

Design, implement, and evaluate a standards-based architecture for secure federated analytics across heterogeneous Trusted Research Environments (TREs) that aligns with the Five Safes Framework, without requiring TREs to modify their internal governance, infrastructure, or operational practices.

In this Perspective, we provide the rationale for the retention of safety-critical certification criteria for health information technology by the Office of the National Coordinator for Health Information Technology (ONC). To not do so puts all future American patients at risk. The proposed deregulatory actions are based on faulty premises, will lead to serious adverse patient outcomes, and sets a dangerous precedent. Public safety regulations should be held to a [...]

To evaluate how cross-terminology mapping of International Classification of Diseases (ICD)-coded diagnoses is associated with preservation of diagnostic distinctions and traceability in electronic health record (EHR)-derived phenotypes for genomic research.

Virtual nursing (VN) is an increasingly popular nursing care model being implemented nationally to help alleviate nursing burden and shortages which are particularly prevalent in emergency care.

To review the clinical evidence for small language models (SLMs), 4 billion parameters or fewer, for performance, safety, and deployment feasibility.

Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automated extraction, but their performance in genome-informed clinical contexts remains incompletely characterized.