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To evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID.

This study develops and validates the confidence-linked and uncertainty-based staged (CLUES) framework by integrating large language models (LLMs) with uncertainty quantification to assist manual chart review while ensuring reliability through a selective human review.

Interruptive clinical decision support (CDS) alerts are intended to improve patient care, but can contribute to alert fatigue, diminishing their effectiveness. The alert demonstrated minimal clinical effect while contributing significantly to alert fatigue.This study aims to evaluate if transitioning a high-firing medication on hold alert from interruptive to noninterruptive would change provider practices.The alert was triggered when at least two medications were held for >48 hours. A pre-post intervention cohort study [...]

In 2023, AMIA's Inclusive Language and Context Style Guidelines (the "Guidelines") were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA's DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share [...]

Fairness concerns stemming from known and unknown biases in healthcare practices have raised questions about the trustworthiness of Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS). Studies have shown unforeseen performance disparities in subpopulations when applied to clinical settings different from training. Existing unfairness mitigation strategies often struggle with scalability and accessibility, while their pursuit of group-level prediction performance parity does not effectively translate into fairness at the point of [...]

Children with a difficult airway are at high risk of decompensation in the setting of respiratory distress. Situational awareness among all team members, and a shared plan in case of an emergency, can reduce the chance of catastrophic outcomes.This study aimed to improve difficult airway situational awareness while minimizing alert burden in a quaternary care pediatric healthcare system through the application of clinical decision support (CDS).Three iterative designs were developed [...]

This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-hospital environments. Metabolic syndrome (MetS) is susceptible to misdiagnosis by DL models due to distribution shifts. This work demonstrates the potential of continual learning (CL) to enhance model performance in MetS identification across diverse settings.

The CONCERN Early Warning System (CONCERN EWS) is an artificial intelligence-based clinical decision support system (AI-CDSS) for the prediction of clinical deterioration, leveraging signals from nursing documentation patterns. While a recent multisite randomized controlled trial (RCT) demonstrated its effectiveness in reducing inpatient mortality and length of stay, evaluating implementation outcomes is essential to ensure equitable results across patient populations.This study aims to (1) assess whether clinicians' usage of the CONCERN [...]