Clinical support system for triage based on federated learning for the Korea triage and acuity scale

Hansol Chang; Jae Yong Yu; Geun Hyeong Lee; Sejin Heo; Se Uk Lee; Sung Yeon Hwang; Hee Yoon; Won Chul Cha; Tae Gun Shin; Min Seob Sim; Ik Joon Jo; Taerim Kim

doi:10.1016/j.heliyon.2023.e19210

Clinical support system for triage based on federated learning for the Korea triage and acuity scale

Heliyon. 2023 Aug 17;9(8):e19210. doi: 10.1016/j.heliyon.2023.e19210. eCollection 2023 Aug.

Authors

Hansol Chang^{1

2}, Jae Yong Yu³, Geun Hyeong Lee^{2

4}, Sejin Heo^{1

2}, Se Uk Lee¹, Sung Yeon Hwang¹, Hee Yoon¹, Won Chul Cha^{1

2

5}, Tae Gun Shin¹, Min Seob Sim¹, Ik Joon Jo¹, Taerim Kim^{1

2}

Affiliations

¹ Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 115 Irwon-ro Gangnam-gu, Seoul, 06355, South Korea.
² Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, 115 Irwon-ro Gangnam-gu, Seoul, 06355, South Korea.
³ Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, South Korea.
⁴ Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, South Korea.
⁵ Digital Innovation Center, Samsung Medical Center, Seoul, Korea. 81 Irwon-ro Gangnam-gu, Seoul 06351, South Korea.

Abstract

Background and aims: This study developed a clinical support system based on federated learning to predict the need for a revised Korea Triage Acuity Scale (KTAS) to facilitate triage.

Methods: This was a retrospective study that used data from 11,952,887 patients in the Korean National Emergency Department Information System (NEDIS) from 2016 to 2018 for model development. Separate cohorts were created based on the emergency medical center level in the NEDIS: regional emergency medical center (REMC), local emergency medical center (LEMC), and local emergency medical institution (LEMI). External and temporal validation used data from emergency department (ED) of the study site from 2019 to 2021. Patient features obtained during the triage process and the initial KTAS scores were used to develop the prediction model. Federated learning was used to rectify the disparity in data quality between EDs. The patient's demographic information, vital signs in triage, mental status, arrival information, and initial KTAS were included in the input feature.

Results: 3,626,154 patients' visits were included in the regional emergency medical center cohort; 8,278,081 patients' visits were included in the local emergency medical center cohort; and 48,652 patients' visits were included in the local emergency medical institution cohort. The study site cohort, which is used for external and temporal validation, included 135,780 patients visits. Among the patients in the REMC and study site cohorts, KTAS level 3 patients accounted for the highest proportion at 42.4% and 45.1%, respectively, whereas in the LEMC and LEMI cohorts, KTAS level 4 patients accounted for the highest proportion. The area under the receiver operating characteristic curve for the prediction model was 0.786, 0.750, and 0.770 in the external and temporal validation. Patients with revised KTAS scores had a higher admission rate and ED mortality rate than those with unaltered KTAS scores.

Conclusions: This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage.

Keywords: Clinical decision-making; Emergency department; Emergency medical service; Machine learning; Triage.