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To share findings of a quality improvement initiative to capture employment status in the electronic medical record (EMR) to mitigate the potential impact of work loss on the health of patients by utilizing the results to identify eligible individuals for resources to assist return-to-work efforts.

The growing use of machine learning (ML) in healthcare raises concerns about how data biases affect real-world model performance. While existing frameworks evaluate algorithmic fairness, they often overlook the impact of bias on generalizability and clinical utility, which are critical for safe deployment. Building on prior methods, this study extends bias analysis to include clinical utility, addressing a key gap between fairness evaluation and decision-making.

To assess whether HL7 Fast Healthcare Interoperability Resources (FHIR) can underpin a fully standards-based, end-to-end digital research architecture, demonstrate it in a live study, and quantify its benefits for interoperability and development efficiency.

Gaps in transportation, particularly public transit, are a significant barrier to accessible, high-quality healthcare. Health systems, payors, and regulatory bodies recognize the need to identify and address these gaps. However, clinical research examining public transportation accessibility and its impacts on healthcare utilization, outcomes, and costs remains limited. Existing tools used for studying public transit are generally non-HIPAA compliant, expensive, proprietary, and/or difficult to use. A tool addressing these concerns is [...]

While timely interventions can improve medication adherence, it is challenging to identify which patients are at risk of nonadherence at point-of-care. We aim to develop and validate flexible machine learning (ML) models to predict a continuous measure of adherence to guideline-directed medication therapies (GDMTs) for heart failure (HF).

Patient navigation is designed to identify and address patients' needs throughout their cancer treatment. In the context of a clinical trial designed to deliver a standardized patient navigation protocol, a registry was needed to allow users from across multiple health systems to input patient data, track navigation outreach, and coordinate cancer care in real time. To design a registry to allow patient navigators (PNs) at 6 medical centers across 4 [...]

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.

Federated network studies allow data to remain locally while the research is conducted through the sharing of analytical code and aggregated results across different health care 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 [...]

This study aimed to leverage a large language model (LLM) to improve the efficiency and thoroughness of medical record documentation. This study focused on aiding clinical staff in creating structured summaries with the help of an LLM and assessing the quality of these artificial intelligence (AI)-proposed records in comparison to those produced by doctors.This strategy involved assembling a team of specialists, including data engineers, physicians, and medical information experts, to [...]

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.