Background: One anesthesiologist performance metric is the incidence of "prolonged" (15 min or longer after dressing complete) times to extubation. The authors used several methods to identify the performance outliers and assess whether targeting these outliers for reduction could improve operating room workflow. Methods: Time to extubation data were retrieved for 27,757 anesthetics and 81 faculty anesthesiologists. Provider-specific incidences of prolonged extubation were assessed by using unadjusted frequentist statistics and a Bayesian model adjusted for prone positioning, American Society of Anesthesiologist's base units, and case duration. Results: 20.31% of extubations were "prolonged," and 40% of anesthesiologists were identified as outliers using a frequentist approach, that is, incidence greater than upper 95% CI (20.71%). With an adjusted Bayesian model, only one anesthesiologist was deemed an outlier. If an average anesthesiologist performed all extubations, the incidence of prolonged extubations would change negligibly (to 20.67%). If the anesthesiologist with the highest incidence of prolonged extubations was replaced with an average anesthesiologist, the change was also negligible (20.01%). Variability among anesthesiologists in the incidence of prolonged extubations was significantly less than among other providers. Conclusions: Bayesian methodology with covariate adjustment is better suited to performance monitoring than an unadjusted, nonhierarchical frequentist approach because it is less likely to identify individuals spuriously as outliers. Targeting outliers in an effort to alter operating room activities is unlikely to have an operational impact (although monitoring may serve other purposes). If change is deemed necessary, it must be made by improving the average behavior of everyone and by focusing on anesthesia providers rather than on faculty.