ArticleFrontiers in medicine2026
Development and preliminary evaluation of an AI-enhanced three-dimensional integrated quality model for quality-sensitive indicators in operating room management: a prospective single-center study.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Traditional quality measurement systems in operating rooms often fail to capture the interdependence among medical equipment performance, operational efficiency, and staff effectiveness. Artificial intelligence and machine learning technologies may strengthen quality-monitoring frameworks when they are embedded within clearly defined operational protocols and human workflow support. Objective: To develop and preliminarily evaluate an artificial intelligence-enhanced three-dimensional integrated quality model for quality-sensitive indicators that integrates real-time equipment monitoring, predictive analytics, and staff performance metrics within an operating room (OR) quality-management intervention. Methods: A prospective single-center study was conducted at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China ( Results: Implementation of the integrated AI-enhanced model was associated with significant improvements across measured domains. Equipment downtime decreased by 37.1% (mean decrease 4.6 events/month, 95% CI: 3.8-5.4, Cohen's d = 1.52, raw and FDR-adjusted Conclusion: The AI-enhanced three-dimensional integrated quality model may offer a structured framework for comprehensive OR quality management when combined with evidence-based maintenance protocols, staff training, and workflow redesign. Given the single-center pre-post design and concurrent implementation components, the findings should be interpreted as improvements observed during an integrated quality-management intervention rather than as the isolated causal effect of AI alone. Controlled multicenter studies are needed to quantify the independent contribution, transferability, and long-term sustainability of the AI components.
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