Multiple triage algorithms have been proposed to optimize the allocation of medical resources in mass casualty incidents. Despite attempts at standardization, first responders often assign patients to triage categories that deviate from those prescribed by these algorithms. This study seeks to understand what patient level factors cause these deviations, and identify clinical factors which cause variance toward over or under triage. Rather than evaluate these decisions against a gold standard, we instead seek to identify patients that cause controversy among first responders with respect to their choices.

This will be an online survey distributed to EMT and Paramedic students in the US. They will be provided with fifty patient cards containing a clinical vignette including description of injuries and vital signs. For each vignette, they will select a triage category (Red, Yellow, Green, or Black.) We will analyze responses to identify areas of controversy, where triage classification showed a significant split between respondents. We can then evaluate these patients for clinical trends.

Data collection and analysis are planned for completion by March 30, 2023.

Identifying patient-level characteristics that contribute to triage variance can allow emergency managers to anticipate under-triage and over-triage following an MCI. This can aid emergency providers as they plan to receive an influx of patients. It also addresses the sub-cognitive biases that impact first responders decision-making, which may aid EMS educators who train first responders in triage.