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Electronic Health Records (EHRs) have significantly impacted healthcare improving access to patient information and enhancing communication among the health care team. However, lack of usability and increased documentation burden has greatly contributed to clinician burnout. Improvements in EHR design that include physician input is critical to develop specific changes that make EHRs less cumbersome; however, it can be challenging to gather input from physicians with full clinical workloads.

The goal of this study was to investigate the association of perceived discrimination with health outcomes and disparities.

This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs).

Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, which is time-consuming, resource-intensive, and may be subject to variability and bias. There is a pressing need for quantitative metrics to enable scalable, objective, and efficient evaluation.

Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated with false positives. This study aims to evaluate the impact of confounders on the specificity of single-outcome prediction models and assess the effectiveness of a multi-class architecture in mitigating outcome conflation.

To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors that can impact their adoption, including overall performance, calibration, fairness, and resilience to privacy protections that reduce data fidelity.

This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

(1) Describe the evolution of Health Information Exchanges (HIEs) into Health Data Utilities (HDUs); (2) Provide motivation for HDUs as a public strategic investment target.