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To evaluate how electronic health records (EHR) optimization and data analytics supported a large rural health system action plan to mitigate the effects of a nation-wide blood culture bottle shortage.

This study aimed to evaluate the characteristics of drug allergy alerts (DAAs) and identify false-positive alerts by analyzing prior drug administration records.

Clinical studies struggle to locate the right patients, in part because many remain undiagnosed or lack relevant labels. We develop and prospectively test a physiology-based patient representation ("Bioprofile") to evaluate whether it can reduce the number of individuals who must be screened and enable more precise targeting of candidates for steatotic liver disease studies.

To assess the performance of a reasoning large language model (LLM) in identifying medication errors in medical incident reports.

To propose a structured framework for evaluating and comparing clinical informatics fellowship programs using the People, Process, and Technology (PPT) model.

The fragmentation of healthcare data in Italy and the increase in demand in emergency departments (EDs) require innovative solutions to improve operational flows, quality of care, and decision-making. This study presents EmergInsight, a prototype interactive real-time dashboard designed to optimize ED operations through the visualization and analysis of clinical and operational data.

Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. This study develops a pipeline for automated note sectioning using open-source large language models (LLMs), focusing on three sections: History of Present Illness, Interval History, and Assessment and Plan.

To compare lightweight open-source large language models (LLMs) with cTAKES, a state-of-the-art natural language processing (NLP) system, in an information extraction task from hospitalization discharge summaries.

Higher education students, particularly those from underrepresented backgrounds, experience heightened levels of anxiety, depression, and burnout. Clinical informatics approaches leveraging K-means clustering can aid in mental health risk stratification, yet they often exacerbate disparities. We present a socially fair clustering framework that ensures equitable clustering costs across demographic groups while minimizing within-cluster variability.