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Population health management programs coordinate care for over 80 million Medicaid beneficiaries but lack systematic clinical decision support for determining when to intervene and which interventions to select for patients with complex conditions. Our objective was to develop and validate a clinical decision support system integrating acuity prediction and intervention selection models for population health management programs.

Ambient digital scribe (ADS) platforms, which combine ambient speech recognition and large language models to generate clinical documentation, are currently undergoing rapid clinical adoption. Early data suggest that ADS utilization may reduce documentation burden and improve provider efficiency; however, the ethical implications of this largely unregulated technology remain relatively unexamined.

To understand the heterogeneous treatment effects of electronic alerts for acute kidney injury (AKI).

We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years).

To develop AutoReporter, a large language model (LLM) system that automates evaluation of adherence to research reporting guidelines.

Trafficked persons experience adverse health consequences and seek help, but many go unrecognized by health-care professionals. This study explored professionals' perspectives on current approaches toward identifying and supporting trafficked persons in health-care settings, highlighting current technology roles, gaps, and future directions.

To develop and evaluate a knowledge graph-augmented large language model (LLM) framework that synthesizes epidemiological evidence to infer life-course exposure-outcome pathways, using gestational diabetes mellitus (GDM) and dementia as a case study.

Documentation of gender identity (GI) and anatomy data in the electronic health record (EHR) is a proposed standard of care for transgender populations. However, there is limited research on implementation of proposed best practices, particularly anatomy data collection. This study aims to characterize factors that influence patient preferences and comfort around the collection and documentation of GI and anatomy in EHRs.

Real-world evidence (RWE) increasingly informs clinical decisions, yet manual adjustment for confounding limits scalability. Data-adaptive (DA) algorithms for high-dimensional proxy adjustment show promise but have not been systematically compared to investigator-specified (IS) approaches across diverse treatment scenarios. We evaluated whether DA strategies perform comparably to manually curated IS models using claims-based emulations of 15 randomized trials from the RCT-DUPLICATE initiative.