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Guidelines recommend measuring glycated hemoglobin (HgbA1c) levels on all inpatients with diabetes mellitus, if untested in the prior 3 months. In response, our health system applies default HgbA1c orders in the electronic insulin orders for qualifying patients. However, recent red blood cell transfusions may cause falsely low HgbA1c values, leading to inappropriate management. Clinicians often forget to deselect default HgbA1c orders after transfusion, leading to erroneous results. We evaluated a [...]

Despite widespread adoption, electronic health records (EHRs) continue to present persistent challenges related to usability, workflow inefficiencies, and clinician burden. Structured EHR optimization programs that address these issues in a scalable and replicable manner remain limited in the literature.This study aimed to describe the development, implementation, and outcomes of the EASY (Eliminate, Automate, Standardize, and Simplify, Y'all) EHR Optimization program-designed to enhance EHR usability, provider satisfaction, and workflow efficiency through [...]

Estimating readmission risk for intensive care unit (ICU) patients is critical for clinicians to optimize resource allocation and prevent premature discharges. Machine learning models currently applied to this task either lack interpretability or cannot identify patient subgroups with distinctive readmission risks and characteristics. We addressed this gap by introducing a cutting-edge rule-based model, namely truly unordered rule sets (TURS), to reveal heterogeneous readmission risks and subgroup-level patient characteristics.

The National Cancer Institute (NCI), part of the National Institutes of Health (NIH) supports efforts to address critical challenges in advancing cancer research. As part of this effort, NCI sponsored the development of a privacy-preserving record linkage (PPRL) software that transforms identifying patient information into multiple tokens through a set of cryptographically secure keyed hash functions. This project aims to evaluate the PPRL software in the perspective of re-identification risks [...]

Large language models' (LLMs') performance in high-stakes, compliance-driven settings such as drafting clinical research documents remains underexplored. This study aims to build a benchmark and an evaluation framework for assessing LLMs' compliance and factuality in generating informed consent forms (ICFs) from clinical trial protocols.

To assess patient attitudes towards ambient artificial intelligence (AI) scribes, including comfort, trust, perceived impact on provider interactions, and willingness for future use, and to examine how sociodemographic, health factors, digital literacy, and privacy concerns shape attitudes.

To qualitatively characterize barriers and facilitators to implementing and using an ambient scribe across a large academic medical center, as well as how ambient transcription reshapes clinicians' perceptions of their work.

Describe the impact of synchronous vs asynchronous clinical decision support (CDS) on clinician behavior in a single-site randomized, controlled environment.

This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep learning fusion model. The model distinguishes between two interaction types: Human-Object Activities (HOA), such as blood pressure measurement, and Human-Human Activities (HHA), such as gland palpation.