Data-Driven Inpatient Bed Assignment Using the P Model

TL;DR

This paper introduces a data-driven, queue-based model for inpatient bed assignment that reduces ED boarding times and minimizes patient overflowing by maximizing the probability of meeting delay targets.

Contribution

It presents a novel, computationally tractable approach to optimize inpatient bed assignment using patient flow data, outperforming traditional policies.

Findings

Reduces patient waiting and boarding times.

Mitigates time-of-day effects on boarding.

Outperforms early discharge and threshold policies.

Abstract

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