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Problem definition: Emergency department (ED) boarding refers to the practiceof holding patients in the ED after they have been admitted to hospital wards,usually resulting from insufficient inpatient resources. Boarded patients maycompete with new patients for medical resources in the ED, compromising thequality of emergency care. A common expedient for mitigating boarding ispatient overflowing, i.e., sending patients to beds in other specialties oraccommodation classes, which may compromise the quality of inpatient care andbring on operational challenges. We study inpatient bed assignment to shortenboarding times without excessive patient overflowing.  Methodology: We use a queue with multiple customer classes and multipleserver pools to model hospital wards. Exploiting patient flow data from ahospital, we propose a computationally tractable approach to formulating thebed assignment problem, where the joint probability of all waiting patientsmeeting their respective delay targets is maximized.  Results: By dynamically adjusting the overflow rate, the proposed approach iscapable not only of reducing patients' waiting times, but also of mitigatingthe time-of-day effect on boarding times. In numerical experiments, ourapproach greatly outperforms both early discharge policies and threshold-basedoverflowing policies, which are commonly used in practice.  Managerial implications: We provide a practicable approach to solving the bedassignment problem. This data-driven approach captures critical features ofpatient flow management, while the resulting optimization problem ispractically solvable. The proposed approach is a useful tool for the control ofqueueing systems with time-sensitive service requirements.

Data-Driven Inpatient Bed Assignment Using the P Model