This innovative approach utilizes large language models to improve the identification of in-hospital cardiac arrest using clinical notes, suggesting a shift from traditional methods.
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.
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Vo et al. (2025) studied this question.
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