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Many primary care physicians (PCPs) feel overwhelmed by the number of electronic health record inbox messages they receive. The objective of this study was to characterize PCPs' experiences with inbox triage-the process of reviewing inbox messages and deciding when and how to address them.

Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive, domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven thematic analysis of social media data.

Overuse and misuse of antibiotics is an urgent health care problem and one of the key factors in antibiotic resistance. Validated clinical prediction rules have shown effectiveness in guiding providers to an appropriate diagnosis and identifying when antibiotics are the recommended choice for treatment.We aimed to study the relative ability of registered nurses using clinical prediction rules to guide the management of acute respiratory infections in a simulated environment compared [...]

Time spent in the electronic health record (EHR) is an important measure of clinical activity. Vendor-derived EHR use metrics may not correspond to actual EHR experience. Raw EHR audit logs enable customized EHR use metrics, but translating discrete timestamps to time intervals is challenging. There are insufficient data available to quantify inactivity between audit log timestamps.This study aimed to develop and validate a computer vision-based model that (1) classifies EHR [...]

To develop a natural language processing (NLP) pipeline for unstructured electronic health record (EHR) data to identify symptoms and functional impacts associated with Long COVID in children.

Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.

Clinical decision support systems (CDSS) are central to modern health care, but their effectiveness is compromised during system downtimes, which affect 96% of health care organizations. During these failures, clinicians lose access to critical decision-making tools like order sets, increasing the risk of medical errors. Traditional downtime solutions, such as paper-based protocols, are often impractical and difficult to maintain.This study introduces and evaluates Offsite Repository for Clinical Assets (ORCA), a [...]

Assess time impact and provider perception of AI-generated encounter summaries.

Type 2 diabetes (T2D) is a growing public health burden with persistent racial and ethnic disparities. . This study assessed the completeness of social determinants of health (SdoH) data for patients with T2D in Epic Cosmos, a nationwide, cross-institutional electronic health recors (EHR) database.

Federated network studies allow data to remain locally while the research is conducted through sharing of analytical code and aggregated results across different healthcare settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have positioned federated [...]