ChatGPT-supported patient triage with voice commands in the emergency department: A prospective multicenter study.

BACKGROUND

Triage aims to prioritize patients according to their medical urgency by accurately evaluating their clinical conditions, managing waiting times efficiently, and improving the overall effectiveness of emergency care. This study aims to assess ChatGPT's performance in patient triage across four emergency departments with varying dynamics and to provide a detailed analysis of its strengths and weaknesses.

METHODS

In this multicenter, prospective study, we compared the triage decisions made by ChatGPT-4o and the triage personnel with the gold standard decisions determined by an emergency medicine (EM) specialist. In the hospitals where we conducted the study, triage teams routinely direct patients to the appropriate ED areas based on the Emergency Severity Index (ESI) system and the hospital's local triage protocols. During the study period, the triage team collected patient data, including chief complaints, comorbidities, and vital signs, and used this information to make the initial triage decisions. An independent physician simultaneously entered the same data into ChatGPT using voice commands. At the same time, an EM specialist, present in the triage room throughout the study period, reviewed the same patient data and determined the gold standard triage decisions, strictly adhering to both the hospital's local protocols and the ESI system. Before initiating the study, we customized ChatGPT for each hospital by designing prompts that incorporated both the general principles of the ESI triage system and the specific triage rules of each hospital. The model's overall, hospital-based, and area-based performance was evaluated, with Cohen's Kappa, F1 score, and performance analyses conducted.

RESULTS

This study included 6657 patients. The overall agreement between triage personnel and GPT-4o with the gold standard was nearly perfect (Cohen's kappa = 0.782 and 0.833, respectively). The overall F1 score was 0.863 for the triage team, while GPT-4 achieved an F1 score of 0.897, demonstrating superior performance. ROC curve analysis showed the lowest performance in the yellow zone of a tertiary hospital (AUC = 0.75) and in the red zone of another tertiary hospital (AUC = 0.78). However, overall, AUC values greater than 0.90 were observed, indicating high accuracy.

CONCLUSION

ChatGPT generally outperformed triage personnel in patient triage across emergency departments with varying conditions, demonstrating high agreement with the gold standard decision. However, in tertiary hospitals, its performance was relatively lower in triaging patients with more complex symptoms, particularly those requiring triage to the yellow and red zones.