Abstract

Simulation-based training is commonly used in medical education. However, there is a gap in knowledge regarding best practices in debriefing. We aimed to identify novel solutions to this by adapting video-assisted debriefing (VAD) methodologies used in athletic training. We hypothesized that utilizing VAD would lead to improvements in performance during advanced cardiac life support (ACLS)-based simulations compared to traditional verbal debriefing (VD). The study was conducted at a single medical school. Participants were fourth-year medical students engaging in ACLS simulation-based training as part of their emergency medicine rotation. After completing an ACLS-based simulation, participants received either VD or VAD and then completed a second simulation scenario. Our primary outcome was ACLS performance, graded by blinded reviewers utilizing a previously developed modified checklist. Secondary outcomes included time from cardiac arrest to initiation of cardiopulmonary resuscitation (CPR) and first defibrillation. Measurements were made before and after the interventional debrief, referred to as pre- and postdebrief. A modified Likert-scale survey was used to subjectively assess the student's overall experience. Forty-six groups of 275 students were included in the study. Mean ACLS performance score for VD and VAD postdebrief were 85% and 82%, respectively (p = 0.27). Mean time from arrest to CPR initiation for VD and VAD postdebrief groups were 20 and 24 s, respectively (p = 0.46). Mean time from arrest to defibrillation for VD and VAD postdebrief groups were 50 and 59 s, respectively (p = 0.39). For the Likert surveys, 85% or more of participants in both groups indicated that the session was "very helpful" in all survey categories. VD and VAD both led to improvements in ACLS performance, time to initiation of CPR, and defibrillation among fourth-year medical students. Though postdebrief results were not statistically significantly different by comparison, overall VD led to greater improvement overall across all outcomes.

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