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Health systems undertaking electronic health record (EHR) transitions often struggle to prepare and support clinicians in learning and using the new system. We evaluated a national peer coaching program-the National EHRM Supplemental Staffing Unit (NESSU)-designed to support clinicians during the U.S. Department of Veterans Affairs' (VA's) transition to a new EHR. Our goal was to assess NESSU's reach, perceived usefulness, and association with key EHR user outcomes, and to characterize [...]

We evaluated the quality and adoption of a large language model (LLM)-based summarization tool for ongoing hospital care.

Prediction models are increasingly used in healthcare for risk stratification and personalized care. Many models are developed using machine learning, which requires tuning hyperparameters to maximize performance based on a chosen loss function metric. In healthcare, the area under the receiver operating characteristic curve (AUROC) is commonly used for this purpose, but it may not always be the most appropriate choice for every clinical application. We empirically characterize whether the [...]

Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process.

To assess whether large language model (LLM)-based clinical trial screening judgments vary by patient sociodemographic characteristics.

Systematic clinical phenotyping using Human Phenotype Ontology (HPO) is central to rare disease diagnosis. However, current disease prioritization (ranking candidate diseases from HPO for a patient) methods face key challenges: they often fail to account for the hierarchical structure of HPO terms, ignore dependencies among correlated terms, and do not adjust for batch effects arising from systematic differences in phenotype documentation across cohorts, institutions, or clinicians. We aim to develop [...]

Machine-learning-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied to address rare outcomes, their impact on probabilistic calibration remains insufficiently understood. This study evaluated the effect of widely used resampling strategies on both discrimination and calibration across real-world clinical prediction tasks.

Data modernization (DM) seeks to transform data and information systems in public health (PH) to support core activities. This study characterizes incumbent PH workers who perform informatics and data-centric roles that are critical to DM activities.

To evaluate whether real-time electronic health record (EHR)-based secure messaging between emergency department (ED) clinicians and prior discharge teams influences ED disposition decisions for patients re-presenting within 30 days of hospital discharge.

To determine whether a greater volume of health information exchange (HIE) between care delivery systems improves patient outcomes and whether effects differ between community care and Veterans Health Administration (VHA) direct care settings.