The operating room case-mix problem under uncertainty and nurses capacity constraints
Zakaria Yahia (),
Amr B. Eltawil () and
Nermine A. Harraz ()
Additional contact information
Zakaria Yahia: Fayoum University
Amr B. Eltawil: Egypt - Japan University of Science and Technology (E-JUST)
Nermine A. Harraz: Alexandria University
Health Care Management Science, 2016, vol. 19, issue 4, No 7, 383-394
Abstract:
Abstract Surgery is one of the key functions in hospitals; it generates significant revenue and admissions to hospitals. In this paper we address the decision of choosing a case-mix for a surgery department. The objective of this study is to generate an optimal case-mix plan of surgery patients with uncertain surgery operations, which includes uncertainty in surgery durations, length of stay, surgery demand and the availability of nurses. In order to obtain an optimal case-mix plan, a stochastic optimization model is proposed and the sample average approximation method is applied. The proposed model is used to determine the number of surgery cases to be weekly served, the amount of operating rooms’ time dedicated to each specialty and the number of ward beds dedicated to each specialty. The optimal case-mix selection criterion is based upon a weighted score taking into account both the waiting list and the historical demand of each patient category. The score aims to maximizing the service level of the operating rooms by increasing the total number of surgery cases that could be served. A computational experiment is presented to demonstrate the performance of the proposed method. The results show that the stochastic model solution outperforms the expected value problem solution. Additional analysis is conducted to study the effect of varying the number of ORs and nurses capacity on the overall ORs’ performance.
Keywords: Healthcare management; Operating rooms scheduling; Stochastic case-mix problem; Mixed integer programming; Stochastic programming; Sample average approximation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)
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DOI: 10.1007/s10729-015-9337-z
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