Manchester Triage System: Effectiveness, Limitations, and Global Implementation—A Structured Narrative Review
Jerica Zaloznik Djordjevic, Zvonka Fekonja, Matej StrnadBackground: The Manchester Triage System (MTS) is one of the most widely used emergency department (ED) triage tools worldwide, aiming to standardize patient prioritization through structured flowcharts and discriminators. Despite its broad adoption, concerns remain regarding its reliability, adaptability, and performance across diverse clinical and operational contexts. Objective: This narrative review aimed to identify and narratively synthesize evidence on the validity, reliability, and contextual performance of the MTS, with particular attention to inter-rater variability, cultural and linguistic adaptation, and operational limitations in real-world ED settings. Methods: A structured literature search was conducted across four databases from inception to January 2026 and analyzed according to the methodological recommendations for narrative biomedical reviews proposed by Gasparyan et al. to identify studies assessing the performance, implementation, and limitations of the MTS across different healthcare systems. Included studies were analyzed qualitatively, focusing on outcome measures related to accuracy, consistency, and contextual applicability. Results: The reviewed evidence supports the MTS’s ability to identify high-acuity patients; however, considerable variability in reliability and performance was observed across settings. Subjectivity in symptom interpretation and discriminator selection, challenges in non-English-speaking environments, and the lack of integration of operational factors, such as ED crowding and staffing levels, were recurrent findings. Conclusions: While the MTS remains a cornerstone of emergency triage, its effectiveness is influenced by human, cultural, and system-level factors. Future research should explore complementary strategies, including enhanced training, local adaptation, point-of-care testing, and artificial intelligence-based decision support, to improve triage consistency and patient outcomes.