Bed Allocation to Reduce Overflow

Posted: 1 Oct 2014

See all articles by Jingui Xie

Jingui Xie

School of Management, University of Science and Technology of China

Mabel C. Chou

National University of Singapore (NUS) - Sustainable & Green Finance Institute (SGFIN)

Marcus Ang

Singapore Management University - Lee Kong Chian School of Business

David Yao

Columbia University

Date Written: September 6, 2014

Abstract

Hospital emergency department boarding time, i.e. the duration between the impatient bed request time and the patient admission time to inpatient wards, is a key performance in many hospitals. In order to avoid this waiting time to exceed certain level, some hospitals including the one under study in this paper may set a maximum boarding time level, say six hours, beyond which patients will be assigned to any available beds in the impatient wards despite the medical specialties required. As a result, patients may be overflowed all over the hospital, causing physicians wasting their time on the way to visit their patients. High overflow rates also cause many other problems such as worse patient outcomes and more complicated bed allocation process. To address the overflow issue, we build an analytical model and propose two easy-to-compute bed allocation polices. We use the real data from the only university hospital in Singapore and a simulation model to evaluate the effectiveness of our proposed polices against the base case provided by the empirical study of the hospital. Through the simulation study, we show that the proposed policies can reduce the overflow rate from 18.91% to about 4-5% without sacrificing other performance measures. More surprisingly, our simulation studies suggest that the existing capacity actually can accommodate 50% more elective patients while keeping the overflow rate at a level of less than 10%.

Keywords: Bed allocation; overflow; queueing models; simulation; data analysis

Suggested Citation

Xie, Jingui and Chou, Mabel C. and Ang, Marcus and Yao, David, Bed Allocation to Reduce Overflow (September 6, 2014). Available at SSRN: https://ssrn.com/abstract=2492478

Jingui Xie (Contact Author)

School of Management, University of Science and Technology of China ( email )

Jinzhai Road No. 96
HEFEI, Anhui 230026
China
86(551)63606983 (Phone)

Mabel C. Chou

National University of Singapore (NUS) - Sustainable & Green Finance Institute (SGFIN) ( email )

Singapore

Marcus Ang

Singapore Management University - Lee Kong Chian School of Business ( email )

469 Bukit Timah Road
Singapore 912409
Singapore

David Yao

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

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