İbrahim Sarbay1, Göksu Bozdereli Berikol2, İbrahim Ulaş Özturan3,4

1Department of Emergency Medicine, Keşan State Hospital, Edirne, Turkey
2Department of Emergency Medicine, Bakırköy Dr. Sadi Konuk Training and Research Hospital, İstanbul, Turkey
3Department of Emergency Medicine, Kocaeli University, Faculty of Medicine, Kocaeli, Turkey
4Department of Medical Education, Acibadem University, Institute of Health Sciences, Istanbul, Turkey

Keywords: Chatbot, ChatGPT, emergency severity index, triage


OBJECTIVES: Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural language. The role of chatbots in health care is deemed worthy of research. OpenAI’s ChatGPT is a supervised and empowered machine learning based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction.

METHODS: This was a preliminary, cross sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over triage (false positive) or under triage (false negative).

RESULTS: Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen’s Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval [CI]: 34–78.2), specificity of 34.5% (95% CI: 17.9–54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8–57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9–75.6), and an F1 score of 0.461. In high acuity cases (ESI 1 and ESI 2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8–91.8), specificity of 93.1% (95% CI: 77.2–99.2), PPV of 88.9% (95% CI: 65.3–98.6), NPV of 84.4 (95% CI: 67.2–94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724–0.969, P < 0.001) for high acuity cases.

CONCLUSION: The performance of ChatGPT was best when predicting high acuity cases (ESI 1 and ESI 2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.

How to cite this article: Sarbay İ, Berikol GB, Özturan İU. Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study. Turk J Emerg Med 2023;23:156-61.

Ethics Committee Approval

Institutional review board approval was obtained for this study on 09.02.2023. (Kocaeli University Non‑Interventional Clinical Research Ethics Committee ‑ GOKAEK‑2023/03.12).
Consent to participate
No informed consent was required for this study.

Author Contributions

İbrahim Sarbay: Conceptualization, methodology, investigation, software, resources, data curation, visualization, writing – review and editing, supervision, Project administration.
Göksu Bozdereli Berikol: Conceptualization, methodology, investigation, software, resources, data curation, visualization, writing – review and editing.
İbrahim Ulaş Özturan: Conceptualization, methodology, formal analysis, investigation, writing – review and editing.

Conflict of Interest

None Declared.

Financial Disclosure