• Da-Young Kang, Kyung-Jae Cho, Oyeon Kwon, Joon-Myoung Kwon, Ki-Hyun Jeon, Hyunho Park, Yeha Lee, Jinsik Park, and Byung-Hee Oh.
• Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, Republic of Korea.
• Scand J Trauma Resus. 2020 Mar 4; 28 (1): 17.

BackgroundIn emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS.MethodsWe conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables.ResultsThe number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]).ConclusionsThe AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.

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