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

Area of Science:

• Healthcare Operations Research
• Surgical Workflow Optimization
• Health Services Research

Background:

• Perioperative care significantly impacts hospital revenue, necessitating efficient operating room (OR) resource utilization.
• Accurate prediction of surgical case duration, including Anesthesia-controlled time (ACT) and Surgical-controlled time (SCT), is crucial for OR scheduling.
• Existing literature often divides ACT and SCT data by surgical specialty, limiting benchmarking and granular analysis.

Purpose of the Study:

• To investigate if analyzing ACT and SCT by specific Current Procedural Terminology (CPT) codes enhances accuracy and reduces variability compared to surgical specialty-level data.
• To provide more precise and less variable data for improved OR schedule modeling and management.
• To generate benchmarking data for ACT and SCT at the CPT code level.

Main Methods:

• A single-center study analyzed 30,091 surgical cases from September 2018 to September 2019 at the University of Colorado Hospital.
• Cases were categorized by the primary attending surgeon's specialty, and Current Procedural Terminology (CPT) codes were obtained from billing data.
• Statistical analyses, including I² and Levene's test, were used to assess the heterogeneity of mean ACT/SCT values and their variances across CPT codes within specialties.

Main Results:

• Significant variability in mean ACT and SCT values was observed between CPT codes within most surgical specialties (excluding Podiatry).
• High variability in the variances of ACT and SCT was also found between CPT codes within surgical specialties.
• Benchmarking values for mean ACT and SCT, along with standard deviations, are presented for granular OR scheduling.

Conclusions:

• Granular data on ACT and SCT, categorized by CPT codes, offers superior accuracy for surgical schedule modeling compared to broader specialty-level data.
• Incorporating the variability of ACT and SCT variances between CPT codes into scheduling models may further enhance predictive accuracy.
• Future research should focus on real-time simulations, logistical modeling, labor utilization, and validation in private practice settings.