Performance of the artificial intelligence-based Swiss medical assessment system versus Manchester triage system in the emergency department: A retrospective analysis

Abstract

BACKGROUND: The emergence of artificial intelligence (AI) offers new opportunities for applications in emergency medicine, including patient triage. This study evaluates the performance of the Swiss Medical Assessment System (SMASS), an AI-based decision-support tool for rapid patient assessment, in comparison with the well-established Manchester Triage System (MTS). METHODS: In this retrospective analysis, patients aged 18 years or above presenting to the Department of Emergency Medicine at Kepler University Hospital in Linz, Austria, during November and December 2024 with non-traumatic complaints were included. Each patient underwent emergency triage using MTS, conducted by a registered nurse, with SMASS applied in parallel. SMASS had no influence on clinical decision-making. RESULTS: In the study period, 1021 patients were triaged with both MTS and SMASS. The mean patient age was 60 years (SD: 21), and 53 % were women. Of the patients categorized as "orange" by MTS, 19 % were classified as non-urgent by SMASS. Conversely, 28 % of the patients triaged as "green" by MTS were classified as urgent by SMASS. Additionally, 23 % of patients classified as non-urgent by SMASS required hospitalization following emergency department evaluation and treatment. Agreement between SMASS and MTS in triaging emergency patients was low as measured by a Cohen's kappa of 0.167. CONCLUSIONS: In this study of patients presenting to a large tertiary-care emergency department, SMASS demonstrated considerable discrepancies in triage classification compared to MTS, with significant rates of both over- and undertriage. Further validation is necessary before integrating AI-based triage tools into routine clinical practice.

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