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Interruptive alerts can negatively impact clinical workflows and contribute to alert fatigue, provider frustration, and burnout. Given that interruptive alert overriding is a heterogeneous and recurring phenomenon, occurring across different organizational contexts with varying characteristics and circumstances, we hypothesize a pragmatic approach with multimodal interventions to address malfunctioning alert populations and maintain those contributing to better patient care.This study aimed to develop a systematic approach to screen, identify, and correct [...]

This study aimed to develop a human factors assessment for medication-related clinical decision support (CDS) based on a previously validated tool that assessed the integration of human factors principles in CDS, the instrument for evaluating human factors principles in medication-related decision support alerts (I-MeDeSA), and pilot it with 10 outpatient clinics across the United States.The human factors assessment was developed based on past validations of I-MeDeSA. Examples included changing the [...]

In the emergency department-initiated buprenorphine for opioid use disorder (EMBED) trial, a clinical decision support (CDS) tool had no effect on rates of buprenorphine initiation in emergency department (ED) patients with opioid use disorder. The Agency for Healthcare Research and Quality (AHRQ) recently released a CDS Performance Measure Inventory to guide data-driven CDS development and evaluation. Through partner co-design, we tailored AHRQ inventory measures to evaluate EMBED CDS performance and [...]

To evaluate and compare different dimensionality reduction techniques for quantifying housing conditions as a social determinant of health (SDOH) across various geographic levels in the United States.

Uncontrolled hypertension is common and frequently related to inadequate adherence to prescribed medications, resulting in suboptimal blood pressure control and increased healthcare utilization. Although healthcare providers have the opportunity to improve medication adherence, they may lack the tools to address adherence at the point of care. This study aims to assess the usability of a digital tool designed to improve medication adherence and blood pressure control among patients with hypertension [...]

Electronic health records (EHRs) have revolutionized clinical practice, but clinicians and institutions have not yet fully optimized their use. Inconsistent documentation of ophthalmic test results can increase potential medicolegal risks if providers bill for tests without properly documenting clinical interpretations.To address this, we developed and implemented a logic tool in Epic (Epic Systems, Verona, Wisconsin, United States) that prompts clinicians to document diagnostic test interpretations as discrete data before closing [...]

Large language models (LLMs) have demonstrated high levels of performance in clinical information extraction compared to rule-based systems and traditional machine-learning approaches, offering scalability, contextualization, and easier deployment. However, most studies rely on proprietary models with privacy concerns and high costs, limiting accessibility. We aim to evaluate 14 publicly available open-source LLMs for extracting clinically relevant findings from free-text echocardiogram reports and examine the feasibility of their implementation in information [...]

Our objective is to compare different pre-training strategies for electronic health record (EHR) foundation models.

Existing ophthalmology studies for clinical phenotypes identification in real-world datasets (RWD) rely exclusively on structured data elements (SDE). We evaluated the performance, generalizability, and fairness of multimodal ensemble models that integrate real-world SDE and free-text data compared to SDE-only models to identify patients with glaucoma.

To address biased representation in Electronic Health Records (EHRs) across subpopulations (SPs), which leads to predictive models underperforming for underrepresented groups, we propose a framework to enhance equitable predictive performance.