دورية أكاديمية
أعرض في EDS

Emergency department triaging using ChatGPT based on emergency severity index principles: a cross-sectional study.

التفاصيل البيبلوغرافية
العنوان: Emergency department triaging using ChatGPT based on emergency severity index principles: a cross-sectional study.
المؤلفون: Colakca C; Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey., Ergın M; Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.; Department of Emergency Medicine, Faculty of Medicine, Yıldırım Beyazit University, Ankara, Turkey., Ozensoy HS; Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey. habibeozensoy@gmail.com., Sener A; Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.; Department of Emergency Medicine, Faculty of Medicine, Yıldırım Beyazit University, Ankara, Turkey., Guru S; Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey., Ozhasenekler A; Department of Emergency Medicine, Ankara Bilkent City Hospital, Ankara, Turkey.; Department of Emergency Medicine, Faculty of Medicine, Yıldırım Beyazit University, Ankara, Turkey.
المصدر: Scientific reports [Sci Rep] 2024 Sep 27; Vol. 14 (1), pp. 22106. Date of Electronic Publication: 2024 Sep 27.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Triage*/methods , Emergency Service, Hospital* , Severity of Illness Index*, Humans ; Cross-Sectional Studies ; Female ; Male ; Middle Aged ; Adult ; Aged ; Artificial Intelligence ; Natural Language Processing ; Aged, 80 and over
مستخلص: Erroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI ® and is being trained for use in natural language processing tasks. Our study aims to determine the accuracy of patient triage using ChatGPT according to the emergency severity index (ESI) for triage in EDs. In our cross-sectional study, 18 years and over patients who consecutively presented to our ED within 24 h were included. Age, gender, admission method, chief complaint, state of consciousness, and comorbidities were recorded on the case form, and the vital signs were detected at the triage desk. A five-member expert committee (EC) was formed from the fourth-year resident physicians. The investigators converted real-time patient information into a standardized case format. The urgency status of the patients was evaluated simultaneously by EC and ChatGPT according to ESI criteria. The median value of the EC decision was accepted as the gold standard. There was a statistically significant moderate agreement between EC and ChatGPT assessments regarding urgency status (Cohen's Kappa = 0.659; P < 0.001). The accuracy between these two assessments was calculated as 76.6%. There was a high degree of agreement between EC and ChatGPT for the prediction of ESI-1 and 2, indicating high acuity (Cohen's Kappa = 0.828). The diagnostic specificity, NPV, and accuracy of ChatGPT were determined as 95.63, 98.17 and 94.90%, respectively, for ESI high acuity categories. Our study shows that ChatGPT can successfully differentiate patients with high urgency. The findings are promising for integrating artificial intelligence-based applications such as ChatGPT into triage processes in EDs.
(© 2024. The Author(s).)
References: Derlet, R. W., Kinser, D., Ray, L., Hamilton, B. & McKenzie, J. Prospective identification and triage of nonemergency patients out of an emergency department: a 5-year study. Ann. Emerg. Med.25 (2), 215–223. https://doi.org/10.1016/S0196-0644(95)70327-6 (1995). (PMID: 10.1016/S0196-0644(95)70327-67832350)
Karcioglu, O. et al. Bir Acil servisin kullanım özellikleri ve başvuran hastaların aciliyetinin hekim ve hasta açısından değerlendirilmesi. Türkiye Acil Tıp Dergisi6 (1), 25–35 (2006).
Bezzina, A. J., Smith, P. B., Cromwell, D. & Eagar, K. Primary care patients in the emergency department: who are they? A review of the definition of the ‘primary care patient’ in the emergency department. Emerg. Med. Aust.17 (5–6), 472–479. https://doi.org/10.1111/j.1742-6723.2005.00779.x (2005). (PMID: 10.1111/j.1742-6723.2005.00779.x)
Gilboy, N., Tanabe, P., Travers, D. A., Rosenau, A. M. & Eitel, D. R. Emergency Severity Index, Version 4: Implementation Handbook. AHRQ Publication No. 05-0046-2 (Agency for Healthcare Research and Quality, 2005).
Sarbay, İ., Berikol, G. B. & Özturan, İ. U. Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): a preliminary, scenario-based cross-sectional study. Turkish J. Emerg. Med.23 (3), 156–161. https://doi.org/10.4103/tjem.tjem&#95;79&#95;23 (2023). (PMID: 10.4103/tjem.tjem_79_23)
Thrall, J. H. et al. Artificial Intelligence and Machine Learning in Radiology: opportunities, challenges, pitfalls, and Criteria for Success. J. Am. Coll. Radiol.15 (3 Pt B), 504–508. https://doi.org/10.1016/j.jacr.2017.12.026 (2018). (PMID: 10.1016/j.jacr.2017.12.02629402533)
Gupta, R. et al. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol. Divers.25 (3), 1315–1360. https://doi.org/10.1007/s11030-021-10217-3 (2021). (PMID: 10.1007/s11030-021-10217-3)
Kose, E., Ozturk, N. N. & Karahan, S. R. Artificial Intelligence in surgery. Eur. Archives Med. Res.34 (0), 4–6. https://doi.org/10.5152/eamr.2018.43043 (2018). (PMID: 10.5152/eamr.2018.43043)
Jartarkar, S. R. Artificial intelligence: its role in dermatopathology. Indian J. Dermatol. Venereol. Leprol.89 (4), 549–552. https://doi.org/10.25259/IJDVL&#95;725&#95;2021 (2023). (PMID: 10.25259/IJDVL_725_202136688886)
OpenAI. GPT-4. Website (2024). https://openai.com/research/gpt-4 [Accessed 05 February 2024].
OpenAI. Introducing ChatGPT 024. Website (2024). https://openai.com/research/gpt-4 [Accessed 05 February 2024].
Zaboli, A., Brigo, F., Sibilio, S., Mian, M. & Turcato, G. Human intelligence versus Chat-GPT: who performs better in correctly classifying patients in triage? Am. J. Emerg. Med.79, 44–47. https://doi.org/10.1016/j.ajem.2024.02.008 (2024). (PMID: 10.1016/j.ajem.2024.02.00838341993)
Fraser, H. et al. Comparison of diagnostic and triage accuracy of ada health and WebMD symptom checkers, ChatGPT, and physicians for patients in an emergency department: Clinical data analysis study. JMIR mHealth uHealth11, e49995. https://doi.org/10.2196/49995 (2023). (PMID: 10.2196/499953778806310582809)
Paslı, S. et al. Assessing the precision of artificial intelligence in ED triage decisions: insights from a study with ChatGPT. Am. J. Emerg. Med.78, 170–175. https://doi.org/10.1016/j.ajem.2024.01.037 (2024). (PMID: 10.1016/j.ajem.2024.01.03738295466)
Ivanov, O. et al. Improving ED emergency severity index acuity assignment using machine learning and clinical natural language processing. J. Emerg. Nurs.47 (2), 265–278e7. https://doi.org/10.1016/j.jen.2020.11.001 (2021). (PMID: 10.1016/j.jen.2020.11.00133358394)
Karlafti, E. et al. Support systems of clinical decisions in the triage of the emergency department using artificial intelligence: the efficiency to support triage. Acta Med. Litu30 (1), 19–25. https://doi.org/10.15388/Amed.2023.30.1.2 (2023). (PMID: 10.15388/Amed.2023.30.1.23757538010417017)
فهرسة مساهمة: Keywords: Artificial intelligence; Emergency department; Emergency severity index; Triage
تواريخ الأحداث: Date Created: 20240927 Date Completed: 20240928 Latest Revision: 20240927
رمز التحديث: 20240928
DOI: 10.1038/s41598-024-73229-7
PMID: 39333599
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-024-73229-7