Evaluating triage system performance is methodologically complex because direct, prospective head-to-head comparisons in the same real-world patient cohorts remain rare, outcomes are heterogeneous, and no universally accepted gold standard for “true” urgency exists. This chapter synthesizes the main performance domains used in the literature, accuracy, discriminatory capacity, and inter-observer reliability, and clarifies how these metrics should be interpreted in context. It then discusses validation frameworks and the strengths and limits of commonly used endpoints, from mortality, admission, ICU admission, and critical events to more proximal surrogates such as life-saving interventions and acute medical treatments. Finally, the chapter compares the typical performance profiles of ESI, MTS, CTAS, ATS, and SATS, emphasizing that observed results reflect both system design and implementation factors such as training and local operational conditions and that no single model is universally superior across settings.

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Triage System Performance

  • Arian Zaboli,
  • Gianni Turcato

摘要

Evaluating triage system performance is methodologically complex because direct, prospective head-to-head comparisons in the same real-world patient cohorts remain rare, outcomes are heterogeneous, and no universally accepted gold standard for “true” urgency exists. This chapter synthesizes the main performance domains used in the literature, accuracy, discriminatory capacity, and inter-observer reliability, and clarifies how these metrics should be interpreted in context. It then discusses validation frameworks and the strengths and limits of commonly used endpoints, from mortality, admission, ICU admission, and critical events to more proximal surrogates such as life-saving interventions and acute medical treatments. Finally, the chapter compares the typical performance profiles of ESI, MTS, CTAS, ATS, and SATS, emphasizing that observed results reflect both system design and implementation factors such as training and local operational conditions and that no single model is universally superior across settings.