Leveraging Large Language Models to Identify In-Hospital Cardiac Arrest

The authors investigate whether large language models can effectively identify in-hospital cardiac arrest (IHCA) from clinical notes, as traditional methods like manual chart abstraction are resource-intensive and diagnosis codes lack sensitivity. They propose a novel approach that leverages these models to enhance the speed and accuracy of IHCA identification, aiming to improve automated detection in clinical settings.