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Physician and advanced practice provider (APP) well-being is a critical focus in healthcare. Emerging technology such as generative artificial intelligence (GAI) scribes reduces physician and APP administrative burden created by electronic health records. Early adopters of this technology have demonstrated promising improvements in clinical documentation, well-being, and cognitive load. However, further exploration across professional roles is warranted.

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.

Health-related social needs (HRSN) significantly influence health outcomes, yet healthcare organizations face persistent challenges tracking referrals to community-based organizations and interpreting referral outcomes across fragmented clinical and social care systems. Prior studies report low referral fulfillment rates, but much of this evidence derives from single organizations, manual data collection, or incentivized documentation workflows, limiting insight into how information infrastructure shapes what is observable at scale.

Patients living with dementia (PLWD) require attention to social determinants of health (SDoH), but social information is often unavailable or incomplete during care encounters, and clinicians report uncertainty about how to act on this information.

In our recent study we showed that GPT's ability to perform OPS-code extraction from operational reports was equivalent to coding neurosurgeons. In this study we aim to evaluate the effect of context enhancement on GPT-5's code extraction abilities.