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=-20.9 to 8.8 min) followed by room setup among circulating nurses (F=1.3, p<0.001; β IQR=-5.2 to 4.4 min) and room setup among scrub nurses (F=1.4, p<0.001; β IQR=-3.7 to 3.2 min). 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.