Operating room (OR) scheduling is a challenging combinatorial problem and hence most optimization-based OR scheduling research makes simplifying assumptions for tractability, including deterministic surgical durations, absence of dynamic emergency arrivals, and the existence of sufficient downstream resources. In this study, we use discrete event simulation to assess the performance of deterministically optimized OR schedules in a network of collaborating hospitals with shared resources, called distributed OR scheduling (DORS), in the face of uncertain surgical durations, emergency arrivals, and limited downstream resources. We quantify the individual and combined disruptive impact of these stochastic factors on the DORS schedule, using real data obtained from the University Health Network (UHN) in Toronto, Canada. We show that the schedule constructed by DORS results in higher OR utilization and lower average surgery cost compared to the simulated current UHN schedule.