Grant: Master's Award: Canada Graduate Scholarships

In the high stakes field of resuscitation medicine, successful patient outcomes rely on expertise in crisis resource management (CRM) skills, including situation awareness (SA) and cognitive load (CL) management. In order to understand the cognitive processes of expert physicians and novice medical students, quantitative and qualitative methods have been developed to assess CL and SA. These methods include Artificial Intelligence-driven adaptive simulation, cognitive task analysis, and measurement of SA using the Situation Awareness Global Assessment Technique (SAGAT). Despite the known importance of the development of these skills in resuscitation medicine, there is a lack of research that integrates adaptive simulation based on real-time physiological measurements of CL, assessment of SA, and post-simulation cognitive task analysis, and validates these tools across the expertise spectrum. This mixed-methods study will compare 4th-year medical students (MS4) and critical care (CCM) and emergency medicine (EP) physicians' performance on two adaptive resuscitation simulations. The study will provide a binary classification of expertise and real-time assessment of CL based on measurements of heart rate variability, galvanic skin response, and eye-tracking metrics. It is hypothesized that for a given set of responses, CCM and EPs will demonstrate more efficient utilization of CL, reduced overall CL, rapid return to baseline after CL peaking, and increased SA compared to MS4s. The results of this study will further our understanding of the quantitative and qualitative measures that separate novice and expert performance in resuscitation medicine. This will help form the foundation for future work in improving the quality of care delivered to critically ill patients.