Multicriteria optimization techniques for understanding the case mix landscape of a hospital
Article
Burdett, Robert L, Corry, Paul, Yarlagadda, Prasad, Cook, David and Birgan, Sean. 2024. "Multicriteria optimization techniques for understanding the case mix landscape of a hospital." European Journal of Operational Research. 319 (1), pp. 263-291. https://doi.org/10.1016/j.ejor.2024.05.030
Article Title | Multicriteria optimization techniques for understanding the case mix landscape of a hospital |
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ERA Journal ID | 148 |
Article Category | Article |
Authors | Burdett, Robert L, Corry, Paul, Yarlagadda, Prasad, Cook, David and Birgan, Sean |
Journal Title | European Journal of Operational Research |
Journal Citation | 319 (1), pp. 263-291 |
Number of Pages | 29 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0377-2217 |
1872-6860 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ejor.2024.05.030 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0377221724003898 |
Abstract | Various medical and surgical units operate in a typical hospital and to treat their patients these units compete for infrastructure like operating rooms (OR) and ward beds. How that competition is regulated affects the capacity and output of a hospital. This article considers the impact of treating different patient case mix (PCM). As each case mix has an economic consequence and a unique profile of hospital resource usage, this consideration is important. To better understand the case mix landscape and to identify those which are optimal from a capacity utilisation perspective, an improved multicriteria optimization (MCO) approach is proposed. As there are many patient types in a typical hospital, the task of generating an archive of non-dominated (i.e., Pareto optimal) case mix is computationally challenging. To generate a better archive, an improved parallelised epsilon constraint method (ECM) is introduced. Our parallel random corrective approach is significantly faster than prior methods and is not restricted to evaluating points on a structured uniform mesh. As such we can generate more solutions. The application of KD-Trees is another new contribution. We use them to perform proximity testing and to store the high dimensional Pareto frontier (PF). For generating, viewing, navigating, and querying an archive, the development of a suitable decision support tool (DST) is proposed and demonstrated. |
Keywords | Hospital capacity assessment; Hospital case-mix planning ; Multi-criteria optimization ; K-D Tree ; OR in health |
ANZSRC Field of Research 2020 | 400303. Biomechanical engineering |
Byline Affiliations | Queensland University of Technology |
University of Southern Queensland | |
Princess Alexandra Hospital, Australia |
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https://research.usq.edu.au/item/z847x/multicriteria-optimization-techniques-for-understanding-the-case-mix-landscape-of-a-hospital
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