Surgical flow disruptions during complex cardiac surgery procedures may cause preventable errors and patient harm. Prior work has shown that timing interruptions during moments of lower cognitive workload (CogL) can minimize the cost of interruption and risk of inducing primary task errors [1,2]. Heart rate variability (HRV) can serve a reliable proxy for cognitive load (CogL) in surgical contexts [3]. We evaluated a novel intelligent interruption management system (IIMS) which estimates surgeons’ CogL from real-time HRV measurements. We hypothesize that IIMS is feasible in a realistic simulation environment and, if implemented in clinical practice, may decrease preventable errors and improve safety.