Assessing clinical acuity in the Emergency Department using the GPT-3.5 Artificial Intelligence Model

Abstract

Abstract This paper evaluates the performance of the Chat Generative Pre-trained Transformer (ChatGPT; GPT-3.5) in accurately identifying higher acuity patients in a real-world clinical context. Using a dataset of 10,000 pairs of patient Emergency Department (ED) visits with varying acuity levels, we demonstrate that GPT-3.5 can successfully determine the patient with higher acuity based on clinical history sections extracted from ED physician notes. The model achieves an accuracy of 84% and an F1 score of 0.83, with improved performance for more disparate acuity scores. Among the 500 pair subsample that was also manually classified by a resident physician, GPT-3.5 achieved similar performance (Accuracy = 0.84; F1 score = 0.85) compared to the physician (Accuracy = 0.86, F1 score = 0.87). Our results suggest that, in real-world settings, GPT-3.5 can perform comparably to physicians on the clinical reasoning task of ED acuity determination.

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