Assessing and Comparing Anesthesiologists' Performance on Mandated Metrics Using a Bayesian Approach

Background: Periodic assessment of performance by anesthesiologists is required by The Joint Commission Ongoing Professional Performance Evaluation program. Methods: The metrics used in this study were the (1) measurement of blood pressure and (2) oxygen saturation (Spo₂) either before or less than 5 min after anesthesia induction. Noncompliance was defined as no measurement within this time interval. The authors assessed the frequency of noncompliance using information from 63,913 cases drawn from the anesthesia information management system. To adjust for differences in patient and procedural characteristics, 135 preoperative variables were analyzed with decision trees. The retained covariate for the blood pressure metric was patient's age and, for Spo₂ metric, was American Society of Anesthesiologist's physical status, whether the patient was coming from an intensive care unit, and whether induction occurred within 5 min of the start of the scheduled workday. A Bayesian hierarchical model, designed to identify anesthesiologists as "performance outliers," after adjustment for covariates, was developed and was compared with frequentist methods. Results: The global incidences of noncompliance (with frequentist 95% CI) were 5.35% (5.17 to 5.53%) for blood pressure and 1.22% (1.14 to 1.30%) for Spo₂ metrics. By using unadjusted rates and frequentist statistics, it was found that up to 43% of anesthesiologists would be deemed noncompliant for the blood pressure metric and 70% of anesthesiologists for the Spo₂ metric. By using Bayesian analyses with covariate adjustment, only 2.44% (1.28 to 3.60%) and 0.00% of the anesthesiologists would be deemed "noncompliant" for blood pressure and Spo₂, respectively. Conclusion: Bayesian hierarchical multivariate methodology with covariate adjustment is better suited to faculty monitoring than the nonhierarchical frequentist approach.