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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.

Implementation and effective use of health information exchange (HIE) has the potential to transform health care and improve patient outcomes across the United States, yet HIEs remain underutilized nationally. The goal of this study was to develop an updated understanding of current barriers and facilitators to HIE implementation, adoption, and use from the perspective of hospital and healthcare leaders.

CURATE.AI is an artificial intelligence platform enabling personalised drug dosing. Aims:1) Determine the feasibility of using CURATE.AI in the outpatient setting.2) Compare the consistency of CURATE.AI recommendations derived from different data sources.3) Assess the alignment of physician and CURATE.AI dosing recommendations.

Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables typically available in EHRs.

The COVID-19 pandemic has highlighted the growing reliance on machine learning (ML) models for predicting disease severity, which is important for clinical decision-making and equitable resource allocation. While achieving high predictive accuracy is important, ensuring fairness in the prediction output of these models is equally important to prevent bias-driven disparities in healthcare. This study evaluates fairness in a machine learning-based COVID-19 severity classification model and proposes an Explainable AI (XAI)-based [...]

The surge in publications increases screening time required to maintain high-quality literature reviews. One of the most time-consuming phases is title and abstract screening. Machine learning tools have semi-automated this process for systematic reviews, with limited success for scoping reviews. ChatGPT, a chatbot based on a large language model, might support scoping review screening by identifying key concepts and themes. We hypothesize that ChatGPT outperforms the semi-automated tool Rayyan, increasing [...]

To describe a health system's experience administering patient-reported outcome measures (PROMs) to patients with a non-English preferred language (NEPL).

We sought to compare and analyze the performance of a medication knowledge base (MKB) vendor's (Medi-Span) fully enabled, factory settings product to operational implementations in hospitals using the Leapfrog Group's Computerized Physician Order Entry (CPOE)/electronic health record (EHR) Evaluation Tool.