Benchmarking of Anesthesia and Surgical Control Times by Current Procedural Terminology (CPT®) Codes

Over half of hospital revenue results from perioperative patient care, thus emphasizing the importance of efficient resource utilization within a hospital’s suite of operating rooms (ORs). Predicting surgical case duration, including Anesthesia-controlled time (ACT) and Surgical-controlled time (SCT) has been significantly detailed throughout the literature as a means to help manage and predict OR scheduling. However, this information has previously been divided by surgical specialty, and only limited benchmarking data regarding ACT and SCT exists. We hypothesized that advancing the granularity of the ACT and SCT from surgical specialty to specific Current Procedural Terminology (CPT^®) codes will produce data that is more accurate, less variable, and therefore more useful for OR schedule modeling and management. This single center study was conducted using times from surgeries performed at the University of Colorado Hospital (UCH) between September 2018 – September 2019. Individual cases were categorized by surgical specialty based on the specialty of the primary attending surgeon and CPT codes were compiled from billing data. Times were calculated as defined by the American Association of Clinical Directors. I² values were calculated to assess heterogeneity of mean ACT and SCT times while Levene’s test was utilized to assess heterogeneity of ACT and SCT variances. Statistical analyses for both ACT and SCT were calculated using JMP Statistical Discovery Software from SAS (Cary, NC) and R v3.6.3 (Vienna, Austria). All surgical cases (n = 87,537) performed at UCH from September 2018 to September 2019 were evaluated and 30,091 cases were included in the final analysis. All surgical subspecialties, with the exception of Podiatry, showed significant variability in ACT and SCT values between CPT codes within each surgical specialty. Furthermore, the variances of ACT and SCT values were also highly variable between CPT codes within each surgical specialty. Finally, benchmarking values of mean ACT and SCT with corresponding standard deviations are provided. Because each mean ACT and SCT value varies significantly between different CPT codes within a surgical specialty, using this granularity of data will likely enable improved accuracy in surgical schedule modeling compared to using mean ACT and SCT values for each surgical specialty as a whole. Furthermore, because there was significant variability of ACT and SCT variances between CPT codes, incorporating variance into surgical schedule modeling may also improve accuracy. Future investigations should include real-time simulations, logistical modeling, and labor utilization analyses as well as validation of benchmarking times in private practice settings.