Reliability of ChatGPT for performing triage task in the emergency department using the Korean Triage and Acuity Scale

Abstract

Background: Artificial intelligence (AI) technology can enable more efficient decision-making in healthcare settings. There is a growing interest in improving the speed and accuracy of AI systems in providing responses for given tasks in healthcare settings. Objective: This study aimed to assess the reliability of ChatGPT in determining emergency department (ED) triage accuracy using the Korean Triage and Acuity Scale (KTAS). Methods: Two hundred and two virtual patient cases were built. The gold standard triage classification for each case was established by an experienced ED physician. Three other human raters (ED paramedics) were involved and rated the virtual cases individually. The virtual cases were also rated by two different versions of the chat generative pre-trained transformer (ChatGPT, 3.5 and 4.0). Inter-rater reliability was examined using Fleiss' kappa and intra-class correlation coefficient (ICC). Results: The kappa values for the agreement between the four human raters and ChatGPTs were .523 (version 4.0) and .320 (version 3.5). Of the five levels, the performance was poor when rating patients at levels 1 and 5, as well as case scenarios with additional text descriptions. There were differences in the accuracy of the different versions of GPTs. The ICC between version 3.5 and the gold standard was .520, and that between version 4.0 and the gold standard was .802. Conclusions: A substantial level of inter-rater reliability was revealed when GPTs were used as KTAS raters. The current study showed the potential of using GPT in emergency healthcare settings. Considering the shortage of experienced manpower, this AI method may help improve triaging accuracy.

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