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Operating room (OR) turnover represents a critical bottleneck in surgical workflows, where delays in transitioning between procedures can cascade into inefficiencies, increased costs, and compromised patient care. This conceptual manuscript introduces a blueprint for multi-agent coordination grounded in constraint-based optimization to streamline OR turnover processes. Drawing from clinical AI architectures and healthcare analytics infrastructures, we propose the constraint-adaptive multi-agent turnover orchestrator (CAMATO). This theoretical framework integrates autonomous agents for real-time task allocation, resource synchronization, and procedural handoffs. CAMATO leverages interoperability frameworks and decision support pipelines to model turnover as a constrained optimization problem, incorporating variables such as staff availability, equipment sterilization cycles, and environmental constraints. The architecture emphasizes governance mechanisms to monitor agent interactions and mitigate coordination failures, ensuring alignment with electronic health record (EHR) intelligence ecosystems. Through interpretive formulas, we conceptualize risk propagation in agent networks, decision confidence under uncertainty, and resource allocation dynamics. This blueprint highlights the potential for enhanced clinical workflow integration without empirical validation, focusing on theoretical implications for scalable, resilient OR management. By synthesizing recent literature on AI-driven healthcare systems, we outline pathways for future architectural refinements in high-stakes clinical environments.

 In the evolving landscape of artificial intelligence (AI) for healthcare, patient-centered approaches are essential to balance preventive care benefits against potential burdens. This conceptual manuscript introduces a novel framework for generating preventive care recommendations through explicit benefit–burden trade-offs, prioritizing individual patient utilities. Drawing from clinical AI architectures, healthcare analytics infrastructures, and electronic health record (EHR) intelligence ecosystems, we propose the patient utility trade-off architecture (PUTA). This multi-layered system integrates decision support pipelines with AI governance and interoperability frameworks. PUTA employs utility-based modeling to quantify benefits such as improved health outcomes and burdens like treatment side effects or resource demands, facilitating personalized recommendations in preventive settings. Theoretical formulas capture decision confidence and burden propagation, ensuring interpretive insights into system dynamics without empirical validation. We synthesize recent literature on clinical workflow integration and monitoring systems, highlighting how PUTA addresses gaps in patient-centered AI deployment. By emphasizing infrastructural uniqueness, including adaptive feedback topologies, this framework advances equitable preventive care. Implications for governance in diverse clinical environments underscore the need for robust data exchange and ethical monitoring, positioning PUTA as a foundational tool for future AI-driven healthcare systems.