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Accurate phenotyping is an essential task for researchers utilizing electronic health record (EHR)-linked biobank programs like the All of Us Research Program to study human genetics. However, little guidance is available on how to select an EHR-based phenotyping procedure that maximizes downstream statistical power. This study aims to estimate accuracy of three phenotype definitions of ovarian, female breast, and colorectal cancers in All of Us (v7 release) and determine which [...]

To characterize the nature and consequence(s) of interdependent physician electronic health record (EHR) work across inpatient shifts.

Healthcare decisions are increasingly made with the assistance of machine learning (ML). ML has been known to have unfairness-inconsistent outcomes across subpopulations. Clinicians interacting with these systems can perpetuate such unfairness by overreliance. Recent work exploring ML suppression-silencing predictions based on auditing the ML-shows promise in mitigating performance issues originating from overreliance. This study aims to evaluate the impact of suppression on collaboration fairness and evaluate ML uncertainty as desiderata [...]

The use of generative large language models (LLMs) with electronic health record (EHR) data is rapidly expanding to support clinical and research tasks. This systematic review characterizes the clinical fields and use cases that have been studied and evaluated to date.

Using 2023-2024 U.S. National Health Interview Survey data, we found that digital health literacy (dHL) mediated nearly half of the difference in telehealth use between Latino adults with non-English and English language preference. These findings identify dHL as a modifiable mechanism linking linguistic and digital access barriers, underscoring the need for multilingual, inclusive, and equitable telehealth design.

Chatbots are increasingly used to deliver health education, patient engagement, and access to healthcare services. GARDE-Chat is an open-source platform designed to facilitate the development, deployment, and dissemination of chatbot-based digital health interventions across different domains and settings.

Despite rapid integration into clinical decision-making, clinical large language models (LLMs) face substantial translational barriers due to insufficient structural characterization and limited external validation.

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.