Keywords
Triage
Emergency Care
How to Cite
Abstract
Background: Triage is a critical clinical tool in emergency care, ensuring appropriate prioritization of patient care. However, traditional triage systems face challenges such as mistriage, resource misallocation, and overcrowding. Evaluation of triage accuracy also lacks a universal reference standard, making comparisons across studies challenging. Artificial intelligence (AI) has shown potential to enhance triage accuracy, mitigate human limitations, and optimize resource allocation. However, routine integration into clinical workflows remains limited, as uptake depends on staff-related factors such as usability, trust, and organizational readiness. Therefore, there is a need to synthesize evidence on both performance and implementation determinants.
Objective: This scoping review compares AI-based triage with conventional/non-AI triage among adults in prehospital and emergency department settings, focusing on implementation determinants as well as performance outcomes. The dual focus on quantitative and qualitative aspects seeks to provide actionable insights for the effective development and integration of AI tools in emergency care.
Methods: The review will follow the Joanna Briggs Institute methodology and the PRISMA-ScR reporting guidelines to ensure transparency. We will search PubMed (MEDLINE), Embase (Ovid), Cochrane Central Register of Controlled Trials (Wiley), Web of Science (Clarivate), Scopus (Elsevier), and grey literature via Google Scholar and ProQuest using four core concepts: AI, triage, emergency medicine, and implementation. We will include studies of adult patients comparing AI-based triage with conventional/non-AI triage in prehospital and emergency department settings. Two reviewers will independently screen titles/abstracts and full-text articles and extract data in Covidence®, with a third reviewer resolving disagreements. Quantitative findings will be summarized descriptively in tables/figures, and heterogeneity in triage accuracy definitions and reference standards will be described explicitly rather than pooling non-comparable measures. Qualitative findings will be synthesized thematically and structured using the Consolidated Framework for Implementation Research (CFIR).
Discussion: This review will map AI-based triage models and key gaps in design, clinical effectiveness, and implementation across prehospital and in-hospital emergency care. By integrating quantitative and qualitative insights, it will bridge theory and practice, guide future research, and foster human-AI collaboration. Findings may inform the development of more effective AI tools that can be integrated into clinical workflows and strengthen acute patient care.
References
Christ M, Grossmann F, Winter D, Bingisser R, Platz E. Modern triage in the emer-gency department. Dtsch Arzteblatt Int. 2010 Dec;107(50):892–8. DOI: 10.3238/arztebl.2010.0892
Faul M, Wald MM, Sullivent EE, Sasser SM, Kapil V, Lerner EB, et al. Large cost savings realized from the 2006 Field Triage Guideline: re-duction in overtriage in U.S. trauma centers. Pre-hosp Emerg Care. 2012;16(2):222–9. DOI: 10.3109/10903127.2011.615013
Berkowitz D, Morrison S, Shaukat H, But-ton K, Stevenson M, LaViolette D, et al. Under-triage: A New Trigger to Drive Quality Improve-ment in the Emergency Department. Pediatr Qual Saf. 2022;7(4):e581. DOI: 10.1097/pq9.0000000000000581
Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K. Emergency depart-ment triage prediction of clinical outcomes using machine learning models. Crit Care Lond Engl. 2019 Feb 22;23(1):64. DOI: 10.1186/s13054-019-2351-7
Sánchez-Salmerón R, Gómez-Urquiza JL, Albendín-García L, Correa-Rodríguez M, Martos-Cabrera MB, Velando-Soriano A, et al. Machine learning methods applied to triage in emergency services: A systematic review. Int Emerg Nurs. 2022 Jan;60:101109. DOI: 10.1016/j.ienj.2021.101109
Mirhaghi A, Heydari A, Mazlom R, Ebrahimi M. The Reliability of the Canadian Tri-age and Acuity Scale: Meta-analysis. North Am J Med Sci. 2015 Jul;7(7):299–305. DOI: 10.4103/1947-2714.161243
Sax DR, Warton EM, Mark DG, Vinson DR, Kene MV, Ballard DW, et al. Evaluation of the Emergency Severity Index in US Emergency Departments for the Rate of Mistriage. JAMA Netw Open. 2023 Mar 1;6(3):e233404. DOI: 10.1001/jamanetworkopen.2023.3404
Moon SH, Shim JL, Park KS, Park CS. Tri-age accuracy and causes of mistriage using the Ko-rean Triage and Acuity Scale. PloS One. 2019;14(9):e0216972. DOI: 10.1371/journal.pone.0216972
Ingielewicz A, Rychlik P, Sieminski M. Drinking from the Holy Grail-Does a Perfect Tri-age System Exist? And Where to Look for It? J Pers Med. 2024 May 31;14(6):590. DOI: 10.3390/jpm14060590
Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inf. 2023 Dec;180:105274. DOI: 10.1016/j.ijmedinf.2023.105274
Yin J, Ngiam KY, Teo HH. Role of Artifi-cial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res. 2021 Apr 22;23(4):e25759. DOI: 10.2196/25759
Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: a narrative re-view. Acute Med Surg. 2022;9(1):e740. DOI: 10.1002/ams2.740
Sheu RK, Pardeshi MS. A Survey on Medi-cal Explainable AI (XAI): Recent Progress, Ex-plainability Approach, Human Interaction and Scoring System. Sensors. 2022 Oct 21;22(20):8068. DOI: 10.3390/s22208068
Eldh AC, Almost J, DeCorby-Watson K, Gifford W, Harvey G, Hasson H, et al. Clinical in-terventions, implementation interventions, and the potential greyness in between -a discussion paper. BMC Health Serv Res. 2017 Jan 7;17(1):16. DOI: 10.1186/s12913-016-1958-5
Beidas RS, Dorsey S, Lewis CC, Lyon AR, Powell BJ, Purtle J, et al. Promises and pitfalls in implementation science from the perspective of US-based researchers: learning from a pre-mortem. Implement Sci IS. 2022 Aug 13;17(1):55. DOI: 10.1186/s13012-022-01226-3
El Arab RA, Al Moosa OA. The role of AI in emergency department triage: An integrative systematic review. Intensive Crit Care Nurs. 2025 Aug;89:104058. DOI: 10.1016/j.iccn.2025.104058
Fatai A, Sattayarom C, Laochai W, Faksook E. Effectiveness of AI-assisted ESI triage on accu-racy and selected outcomes in emergency nursing: A systematic review. Int Emerg Nurs. 2025 Dec;83:101680. DOI: 10.1016/j.ienj.2025.101680
Peters MDJ, Godfrey CM, Khalil H, McIn-erney P, Parker D, Soares CB. Guidance for con-ducting systematic scoping reviews. Int J Evid Based Healthc. 2015 Sep;13(3):141–6. DOI: 10.1097/XEB.0000000000000050
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 2;169(7):467–73. DOI: 10.7326/M18-0850
Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated Consolidated Framework for Implementation Research based on user feedback. Implement Sci IS. 2022 Oct 29;17(1):75. DOI: 10.1186/s13012-022-01245-0
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