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Original Research

Open Access

Improving triage accuracy of hospitalization and discharge decisions in the emergency department

  • Sung-Joon Park1
  • Sung-Hyuk Choi1,*,
  • Dae-Jin Song2
  • Jong-Hak Park3
  • Ju-Hyun Song3
  • Han-Jin Cho3
  • Sun-Hong Lee4
  • Byung-Chul Ko5
  • Kyu-Hwan Ahn6
  • Gil-Gon Kim7
  • Won-Seok Choi7
  • Kyung-Nam Kim8

1Department of Emergency Medicine, Korea University Guro Hospital, 08308 Seoul, Republic of Korea

2Department of Pediatrics, Korea University Guro Hospital, 08308 Seoul, Republic of Korea

3Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan, Republic of Korea

4Institute for trauma research, Korea University, 02841 Seoul, Republic of Korea

5Poderosa Co., Ltd, 06626 Seoul, Republic of Korea

6WeAreFriends, Co., Ltd, 05635 Seoul, Republic of Korea

7InnoRules Co., Ltd, 05855 Seoul, Republic of Korea

8Waycen Inc., 06182 Seoul, Republic of Korea

DOI: 10.22514/sv.2023.015

Submitted: 13 September 2022 Accepted: 25 November 2022

Online publish date: 22 February 2023

*Corresponding Author(s): Sung-Hyuk Choi E-mail:


The initial severity triage of patients in the emergency department (ED) is implemented differently worldwide, according to the characteristics of each country. However, better classification methods are being studied due to various problems with the current system. Therefore, the aim of this study was to determine the usefulness of patients’ severity assessment in a new way that gives appropriate values to factors that can be obtained in the ED.We collected data from 158,246 patients who visited the ED from 01 January 2016, to 31 December 2020. Using the appropriate values of various factors obtained using the Rasch analysis method, the cut-off values for predicting hospitalization and discharge at the ED of patients were determined. Furthermore, using artificial intelligence, the patients who were hospitalized and discharged from the ED were classified and compared with the results of the Rasch analysis. The accuracy of the algorithms was analyzed as a combination of factors that could be obtained during the initial stage of the patient’s visits. The area under the curve (AUC) value for the prediction of hospitalization and discharge by a combination of factors immediately obtained from the visit was 0.611. In addition, using the factors that could be obtained after a certain period, the AUC value of hospitalization and discharge prediction was 0.767. The results of analysis using artificial intelligence were similar to or slightly higher than the AUC value of the Rasch analysis. The prediction of hospitalization and discharge in the ED using clinical parameters with Rasch analysis can be used for medical assistance, which is expected to help in the efficient operation of the ED.


Triage; Severity; Rasch analysis; Hospitalization; Emergency; Patient characteristics

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Sung-Joon Park,Sung-Hyuk Choi,Dae-Jin Song,Jong-Hak Park,Ju-Hyun Song,Han-Jin Cho,Sun-Hong Lee,Byung-Chul Ko,Kyu-Hwan Ahn,Gil-Gon Kim,Won-Seok Choi,Kyung-Nam Kim. Improving triage accuracy of hospitalization and discharge decisions in the emergency department. Signa Vitae. 2023.doi:10.22514/sv.2023.015.


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