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To measure hospital engagement in interoperable exchange of health-related social needs (HRSN) data.

This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language models to create secure, Health Insurance Portability and Accountability Act-compliant synthetic patient notes for global biomedical research.

Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

Electronic health records (EHRs) data are increasingly used for research and analysis, but there is little empirical evidence to inform how automated and manual assessments can be combined to efficiently assess data quality in large EHR repositories.

While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.

This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs).