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To validate a phenotyping algorithm for gradations of diverticular disease severity and investigate relationships between unmet social needs and disease severity.

Electronic health records (EHRs) data are increasingly used for research and analysis, but there is little empirical evidence to inform how automated and manual assessments can be combined to efficiently assess data quality in large EHR repositories.

While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.

Although biomedical informatics has multiple roles to play in addressing the climate crisis, collaborative action and research agendas have yet to be developed. As a first step, AMIA's new Climate, Health, and Informatics Working Group held a mini-summit entitled Climate and health: How can informatics help? during the AMIA 2023 Fall Symposium to define an initial set of areas of interest and begin mobilizing informaticians to confront the urgent challenges [...]

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.

Many children with cancer are treated as part of interventional clinical trials. Ensuring that the correct chemotherapy treatment plan is used is paramount.

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

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.

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).