Artificial intelligence-driven prescriptive model to optimize team efficiency in a high-volume primary arthroplasty practice.

Abstract

We aimed to improve OR efficiency using machine learning (ML) to find relevant metrics influencing surgery time success and team performance on efficiency to create a model which incorporated team, patient, and surgery-related factors. From 2012 to 2020, five surgeons, 44 nurses, and 152 anesthesiologists participated in 1199 fourjoint days (4796 cases): 1461 THA, 1496 TKA, 652 HR, 242 UKA, and 945 others. Patients were 2461f:2335m; age, 64.1; BMI, 29.93; and ASA, 2.45. Surgical Success was defined as completing four joints within an eight hour shift using one OR. Time data was recorded prospectively using Surgical Information Management Systems. Hospital records provided team, patient demographics, adverse events, and anesthetic. Data mining identified patterns and relationships in higher dimensions. Predictive analytics used ML ranking algorithm to identify important metrics and created decision tree models for benchmarks and success probability. Five variables predicted success: anaesthesia preparation time, surgical preparation time, time of procedure, anesthesia finish time, and type of joint replacement. The model determined success rate with accuracy of 72% and AUC = 0.72. Probability of success based on mean performance was 77-89% (mean-median) if APT 14-15minutes, PT 68-70minutes, AFT four to five minutes, and turnover 25-27minutes. With the above benchmarks maintained, success rate was 59% if surgeon exceeded 71.5-minutes PT or 89% if 64-minutes procedure time or 66% when anesthesiologist spent 17-19.5minutes on APT. AI-ML predicted OR success without increasing resources. Benchmarks track OR performance, demonstrate effects of strategic changes, guide decisions, and provide teamwork improvement opportunities.