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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.

Hospital housekeeping demand is closely tied to bed turnover, discharge timing, isolation precautions, and unit-level patient movement. Delays in environmental services completion can slow bed availability and create downstream pressure on emergency departments, inpatient units, and procedural areas. Environmental services staffing is often managed through fixed shift patterns, current occupancy views, and reactive dispatch queues. These approaches may not anticipate cleaning surges caused by clustered discharges, isolation rooms, or changing census patterns. This manuscript proposes a predictive model for forecasting hospital housekeeping demand by combining discharge predictions, room turnover history, isolation status, environmental cleaning requirements, and unit-level census signals. The goal is to estimate the number, type, and timing of cleaning tasks needed across hospital units. The proposed model would use historical environmental services logs, admission-discharge-transfer data, bed management data, discharge prediction outputs, and infection-control status indicators. A supervised regression or time-series architecture could generate hourly unit-level demand forecasts for routine, terminal, and enhanced cleaning tasks. Conceptually, the model would be expected to identify upcoming cleaning pressure before it appears on the live dispatch board. For example, it could anticipate an afternoon surge in terminal cleans when several predicted discharges coincide with isolation rooms and high unit census. A forecasting model for housekeeping demand could support proactive environmental services staffing, reduce avoidable bed turnaround delays, and improve hospital throughput. Its value would depend on careful integration with existing bed management systems and prospective evaluation in operational settings.