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Clinical data exchange remains fragmented and insufficient to support coordinated, safe care. Decades of technological progress, standards development, and interoperability-focused legislative and regulatory improvements have been incremental, underscoring the need for unifying initiatives to coordinate and improve operations and outcomes.

Falls are the leading cause of injury and injury-related death among older adults. Clinical decision support (CDS) may improve clinician referral and patient access to fall prevention exercise.

Clinicians frequently face questions that require rapid, evidence-based answers. Artificial intelligence (AI) tools are increasingly used for this purpose, yet their reliability for clinical decision-making remains uncertain.

A potential limitation of current data segmentation technologies is that pertinent contextual information accessible via the patient's electronic health records (EHRs) is not used to make segmentation inferences.

Protecting sensitive health information, particularly substance use data, is essential to preserving patient trust, improving care engagement, and ensuring compliance with federal confidentiality laws such as 42 CFR Part 2. Manual segmentation of sensitive data remains burdensome and error-prone, creating a need for scalable, automated, and patient-centered data segmentation technologies.

Physician burnout, often driven by documentation burden in electronic health records, remains a major challenge in clinical care. Ambient artificial intelligence (AI) scribes generate draft notes from clinical encounters, offering a scalable approach to reduce administrative workload, yet population-level evaluations remain limited.

Our study aims to assess the time-cost burden reduction of transitioning from manual case reporting to electronic case reporting (eCR) for COVID-19 among healthcare organizations (HCOs) over a 1-year period.

Efficient exchange of health information requires consistent representation of clinical concepts across laboratories, hospitals, and public health systems. LOINC supports this interoperability by standardizing laboratory test codes, but mapping remains difficult when datasets are incomplete, inconsistently formatted, or structurally diverse. These challenges often create a mismatch between algorithmic performance in controlled settings and real-world deployment. This study aimed to develop a biomedical natural language processing (NLP) approach for mapping heterogeneous [...]

To comprehensively evaluate the validity of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for both prevalent diagnoses and less common diseases, and to assess the performance of a large language model (LLM)-based system in validating these codes.