ArticleBMJ health & care informatics2025
Measuring provider-level differences in perioperative workflow using computer vision-based artificial intelligence.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development of an Artificial Intelligence Platform for Surgical Tool Detection and Phase Recognition in Laparoscopic Distal Gastrectomy.Journal of gastric cancer · 2026Article
- Implementation of Image-Based Artificial Intelligence Is Associated with Increased Case Volume in a High-Acuity, 15-Room Cardiothoracic Operating Suite at a Tertiary Academic Hospital.Journal of imaging · 2026Article
- Automated monitoring in clinical and operational workflows.BMJ health & care informatics · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo 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.
methodsA 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.
resultsThe 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).
conclusionsAutomated 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.
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