Improving triage accuracy of hospitalization and discharge decisions in the emergency department
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 Vol.19,Issue 5,September 2023 pp.75-90
Submitted: 13 September 2022 Accepted: 25 November 2022
Published: 08 September 2023
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
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. 19(5);75-90.
 Chen W, Linthicum B, Argon NT, Bohrmann T, Lopiano K, Mehrotra A, et al. The effects of emergency department crowding on triage and hospital admission decisions. The American Journal of Emergency Medicine. 2020; 38: 774–779.
 Kuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems: variation in assessment of validity. Emergency Medicine Journal. 2017; 34: 703–710.
 Tomyn AJ, Stokes MA, Cummins RA, Dias PC. A Rasch analysis of the personal well-being index in school children. Evaluation & the Health Professions. 2020; 43: 110–119.
 Ambrosio L, Rodriguez-Blazquez C, Ayala A, Forjaz MJ. Rasch analysis of the living with chronic illness scale in Parkinson’s disease. BMC Neurology. 2020; 20: 346.
 Medvedev O, Turner-Stokes L, Ashford S, Siegert RJ. Rasch analysis of the UK functional assessment measure in patients with complex disability after stroke. Journal of Rehabilitation Medicine. 2018; 50: 420–428.
 Liang W, Yao J, Chen A, Lv Q, Zanin M, Liu J, et al. Early triage of critically ill COVID-19 patients using deep learning. Nature Communications. 2020; 15: 3543.
 Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care. 2019; 23: 64.
 Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Annals of Emergency Medicine. 2018; 71: 565–574.e2.
 Azeredo TRM, Guedes HM, Rebelo de Almeida RA, Chianca TCM, Martins JCA. Efficacy of the Manchester Triage System: a systematic review. International Emergency Nursing. 2015; 23: 47–52.
 Jesus APS, Okuno MFP, Campanharo CRV, Lopes MCBT, Batista REA. Manchester Triage System: assessment in an emergency hospital service. Revista Brasileira de Enfermagem. 2021; 74: e20201361.
 Moon SH, Shim JL, Park KS, Park CS. Triage accuracy and causes of mistriage using the Korean triage and acuity scale. PLoS One. 2019; 14: e0216972.
 Caramello V, Marulli G, Reimondo G, Fanto’ F, Boccuzzi A. Inpatient disposition in overcrowded hospitals: is it safe and effective to use reverse triage and readmission screening tools for appropriate discharge?An observational prospective study of an Italian II level hospital. International Journal of Clinical Practice. 2019; 73: e13281.
 Iversen AKS, Kristensen M, Østervig RM, Køber L, Sölétormos G, Lundager Forberg J, et al. A simple clinical assessment is superior to systematic triage in prediction of mortality in the emergency department. Emergency Medicine Journal. 2019; 36: 66–71.
 Fernandes CM, Wuerz R, Clark S, Djurdjev O. How reliable is emergency department triage? Annals of Emergency Medicine. 1999; 34: 141–147.
 Tam HL, Chung SF, Lou CK. A review of triage accuracy and future direction. BMC Emergency Medicine. 2018; 18: 58.
 Christ M, Grossmann F, Winter D, Bingisser R, Platz E. Modern triage in the emergency department. Deutsches Ärzteblatt International. 2010; 107: 892–898.
 Kumar A, Lakshminarayanan D, Joshi N, Valid S, Bhoi S, Deorari A. Triaging the triage: reducing waiting time to triage in the emergency department at tertiary care hospital in New Delhi, India. Emergency Medicine Journal. 2019; 36: 558–563.
 Dong Sl, Bullard MJ, Meurer DP, Colman I, Blitz S, Holroyd BR, et al. Emeregncy triage: comparing a novel computer triage program with standard triage. Academic Emergency Medicine. 2005; 12: 502–507.
 Rutschmann OT, Hugli OW, Marti C, Grosgurin O, Geissbuhler A, Kossovsky M, et al. Reliability of the revised Swiss emergency triage scale: computer simulation study. European Journal of Emergency Medicine. 2018; 25: 264–269.
 Montandon DS, de Souza-Junior VD, Dos Santos Almeida RG, Marchi-Alves LM, Costa Mendes IA, de Godoy S. How to perform prehospital emergency telephone triage: a systematic review. Journal of Trauma Nursing. 2019; 26: 104–110.
 Duke T. New WHO guidelines on emergency triage and assessment and treatment. Lancet. 2016; 387: 721–724.
 Wongpakaran N, Wongpakaran T, Kuntawong P. A short screening tool for borderline personality disorder (Short-Bord): validated by Rasch analysis. Asian Journal of Psychiatry. 2019; 44: 195–199.
 Ahn E, Kim J, Rahman K, Baldacchino T, Baird C. Development of a risk predictive scoring system to identify patients at risk of representation to emergency department: a retrospective population-based analysis in Australia. BMJ Open. 2018; 8: e021323.
Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,200 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.
Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.
Chemical Abstracts Service Source Index The CAS Source Index (CASSI) Search Tool is an online resource that can quickly identify or confirm journal titles and abbreviations for publications indexed by CAS since 1907, including serial and non-serial scientific and technical publications.
Index Copernicus The Index Copernicus International (ICI) Journals database’s is an international indexation database of scientific journals. It covered international scientific journals which divided into general information, contents of individual issues, detailed bibliography (references) sections for every publication, as well as full texts of publications in the form of attached files (optional). For now, there are more than 58,000 scientific journals registered at ICI.
Geneva Foundation for Medical Education and Research The Geneva Foundation for Medical Education and Research (GFMER) is a non-profit organization established in 2002 and it works in close collaboration with the World Health Organization (WHO). The overall objectives of the Foundation are to promote and develop health education and research programs.
Scopus: CiteScore 1.0 (2022) Scopus is Elsevier's abstract and citation database launched in 2004. Scopus covers nearly 36,377 titles (22,794 active titles and 13,583 Inactive titles) from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences and health sciences.
Embase Embase (often styled EMBASE for Excerpta Medica dataBASE), produced by Elsevier, is a biomedical and pharmacological database of published literature designed to support information managers and pharmacovigilance in complying with the regulatory requirements of a licensed drug.