Human Factors Design and Evaluation of Augmented Reality for Mass Casualty Incident Triage

Abstract

Augmented reality (AR) is an emerging technology with immense potential for enhancing human-to-human interaction tasks, particularly in high-risk environments such as mass casualty incident (MCI) tri-age. However, developing practical and effective AR tools for this purpose necessitates a meticulous user-centered design (UCD) process, thoughtfully crafted and validated through iterative testing with first responders in increasingly contextually relevant simulations. In academic circles, the perceived complexity and time requirements of such a process might discourage its adoption within the constraints of traditional publishing cycles. This is likely due, in part, to a lack of representative applied UCD examples. This work addresses this challenge by presenting a scholarly UCD framework tailored specifically for MCI triage, which progresses seamlessly from controlled, context-free laboratory settings to virtual patient simulations and finally to realistic patient (actor) scenarios. Moreover, MCIs and triage are under-served areas, likely due to their high intensity and risk. This means developers need to 'get it right' as quickly as possible. UCD and evaluation alone are not an efficient means to developing these complex and dangerous work domains. Thus, this research also delves into a cognitive work analysis, offering a comprehensive breakdown of the MCI triage domain and how those findings inform future AR sup-ports. This analysis serves to fortify the foundation for future UCD endeavors in this critical space. Finally, it is imperative to recognize that MCI triage fundamentally involves human-to-human interaction supported by AR technology. Therefore, UCD efforts must encompass a diverse array of study stimuli and participants to ensure that the technology functions as intended across all demographic groups. It is established that racial bias exists in emergency room triage, creating worse outcomes for patients of color. Consequently, this study also investigates the potential impact of racial biases on MCI triage efficacy. This entire body of work has implications for UCD evaluation methodology, the development of future AR support tools, and the potential to catch racially biased negative performance before responders ever hit the field.