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Prescription drug monitoring programs (PDMPs) offer a promising data source for the development of clinical decision support (CDS) systems to guide opioid prescribing, but most PDMP agencies lack capacity for in-house model development-instead relying on third-party tools. This study aims to externally validate a PDMP-based model, developed using 2018 California PDMP records, to predict new long-term use among Kentucky opioid-naïve patients and compare its performance to Kentucky-specific machine learning (ML) [...]

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 examine the associations of medical discrimination with hypertension (HTN) outcomes.

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.

To evaluate the feasibility and applicability of the studyathon format within a pharmaceutical company setting for generating reliable real-world evidence rapidly for a business-relevant research question.

Transformer-based models for clinical prediction using longitudinal electronic health record (EHR) data are often developed with large feature sets and long patient histories under the assumption that more data improves performance. However, high-dimensional inputs and long sequences increase computational burden, potentially limiting scalability. We evaluated how feature dimensionality and sequence length affect predictive performance, calibration, risk stratification, and computational efficiency in EHR prediction.

To develop and validate a weighting framework to improve representativeness of Oracle Health Real-World Data (OHRWD).

This study aimed to develop large language models (LLMs) to automatically identify antiretroviral therapy (ART) adherence barriers and stratify nonadherence risk levels from patient-generated messages.