A mixed integer linear programing approach to perform hospital capacity assessments. (1st July 2017)

• Record Type: Journal Article
• Authors: Burdett, Robert L.; Kozan, Erhan; Sinnott, Michael; Cook, David; Tian, Yu-Chu
• Is Part Of: Expert systems with applications. Volume 77(2017)
• Page Start: 170
• Page End: 188
• Publication Date: 2017-07-01
• Subjects: Capacity analysis -- Theoretical capacity -- Health care -- Hospitals -- Hospital resource planning -- Capacity querying; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.01.050
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Analytical decision support models are introduced for hospital capacity analysis. Patient care plans are used to describe different types of patients. Capacity querying, buffering and sensitivity analysis methods have been devised. Resource and bed-space assignments are by-products of the model. The capacity models have been comprehensively tested on a real life case study. 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.