A mixed integer linear programing approach to perform hospital capacity assessments |
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| Affiliation: | 1. School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, 2 George Street Brisbane Qld 4000, Australia;2. Emergency Medicine, Princess Alexandra Hospital, 2 Ipswich Rd, Woolloongabba, Brisbane, Qld 4102, Australia;3. Intensive Care Unit, Princess Alexandra Hospital, 2 Ipswich Rd, Woolloongabba, Brisbane, Qld 4102, Australia;4. School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, 2 George Street Brisbane Qld 4000, Australia;1. Intelligent Data Analytics Research Program Dept. Aselsan Research Center, Ankara, Turkey;2. Department of Electrical Engineering, Stanford University, CA, USA;3. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey;4. National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey;1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea;2. Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea;1. Research Center of Computational Perception and Cognition, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, PR China;2. Information and Computer Engineering College, Northeast Forestry University, Harbin 150001, PR China |
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| Abstract: | An approach to perform a system wide analysis of hospital resources and capacity has been developed. Embedded within an intelligent system it would provide planners and management capability to strategically improve the efficiency of their hospitals today and a means to create more efficient hospitals in the future. In theory, this approach can help hospitals with a variety of capacity planning and resource allocation activities. On a day to day basis it can be used to perform a variety of important capacity querying activities. In addition, it can be used to predict the future performance of a hospital and the effect of structural and parametric changes within the hospital. The approach consists of a mixed integer linear programming (MILP) model and a number of advanced extensions. The MILP models can determine the maximum number of patients of each type that can be treated within a given period of time or the time required to process a given cohort of patients. A case study of a large public hospital has been performed to validate our approach. Extensive numerical investigations successfully demonstrate the applicability of the approach to real sized health care applications and the great potential for further research and development on this topic. |
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