Leveraging Large Language Models to Identify In-Hospital Cardiac Arrest

Manual chart abstraction is the gold standard for identifying in-hospital cardiac arrest (IHCA) but is resource-intensive. Diagnosis codes are a widely used alternative given their accessibility and automated nature, but this method has poor sensitivity and positive predictive value. We present a novel large language model (LLM) approach to identify IHCA and location, highlighting the potential of LLMs for rapid, accurate, and automated IHCA identification from clinical notes.