A Robust Optimization Model for Managing Elective Admission in a Public Hospital

Fanwen Meng (), Jin Qi (), Meilin Zhang (), James Ang (), Singfat Chu () and Melvyn Sim ()
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Fanwen Meng: Department of Health Services and Outcomes Research, National Healthcare Group, Singapore 138543
Jin Qi: Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Hong Kong
Meilin Zhang: NUS Business School, National University of Singapore, Singapore 119077
James Ang: NUS Business School, National University of Singapore, Singapore 119077
Singfat Chu: NUS Business School, National University of Singapore
Melvyn Sim: NUS Business School, National University of Singapore

Operations Research, 2015, vol. 63, issue 6, 1452-1467

Abstract: The admission of emergency patients in a hospital is unscheduled, urgent, and takes priority over elective patients, who are usually scheduled several days in advance. Hospital beds are a critical resource, and the management of elective admissions by enforcing quotas could reduce incidents of shortfall. We propose a distributionally robust optimization approach for managing elective admissions to determine these quotas. Based on an ambiguous set of probability distributions, we propose an optimized budget of variation approach that maximizes the level of uncertainty the admission system can withstand without violating the expected bed shortfall constraint. We solve the robust optimization model by deriving a second order conic problem (SOCP) equivalent of the model. The proposed model is tested in simulations based on real hospital admission data, and we report favorable results for adopting the robust optimization models.

Keywords: robust optimization; elective admission; healthcare; hospital operations (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (24)

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