A mixed integer linear programing approach to perform hospital capacity assessments
| Autor: | David Cook, Michael Sinnott, Robert L. Burdett, Yu-Chu Tian, Erhan Kozan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Decision support system
021103 operations research Capacity assessment Linear programming Operations research Computer science Management science Process (engineering) 0211 other engineering and technologies General Engineering 02 engineering and technology Computer Science Applications Capacity planning Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Resource allocation 020201 artificial intelligence & image processing Integer programming Integer (computer science) |
| Zdroj: | Expert Systems with Applications. 77:170-188 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2017.01.050 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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