Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence

Objectives To evaluate provider-level variability across the full perioperative workflow using a computer vision-based artificial intelligence (AI) system that automatically detects and timestamps operating room events. Methods A cross-sectional study of total knee arthroplasty cases performed between September 2022 and March 2025 at a regional health system was conducted. An ambient surgical platform equipped with wall-mounted cameras continuously captured perioperative activity. A YOLO-based model identified patients, staff and equipment, and a transformer-based event detector predicted key perioperative events in real time. Detected events were used to segment cases into eight workflow phases: anaesthesia induction, patient preparation, final preparation, active procedure, postoperation, patient exit, room cleanup and room setup. Provider-level variability in segment durations was evaluated after adjusting for case characteristics, daily surgical volume and team composition. Results The computer vision event detection system achieved high agreement with ground truth annotations. Across 2502 surgical cases, significant provider-level variability was observed in all workflow segments except for room exit. Active procedure showed the greatest variation among surgeons (F=28.4, p<0.001; β IQR=−20.9to 8.8min) followed by room setup among circulating nurses (F=1.3, p<0.001; β IQR=−5.2to 4.4min) and room setup among scrub nurses (F=1.4, p<0.001; β IQR=−3.7to 3.2min). Conclusions Automated workflow segmentation using computer vision provides a scalable method to evaluate perioperative efficiency with greater granularity. Broader case segmentation may support more targeted and effective surgical quality improvement initiatives.