A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

Muhammed Ordu, Eren Demir, Chris Tofallis, Murat Gunal

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
165 Downloads (Pure)

Abstract

The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Increasing demand and capacity shortages are experienced across all specialties and services in hospitals. Modelling at this level of detail is a necessity, as all the services are interconnected, and cannot be assumed to be independent of each other. Our review of the literature revealed two facts; First an entire hospital model is rare, and second, use of multiple OR techniques are applied more frequently in recent years. Hybrid models which combine forecasting, simulation and optimization are becoming more popular. We developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of, (1) forecasting demand for all the specialties, (2) capturing all the uncertainties of patient pathway within a hospital setting using discrete event simulation, and (3) developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from simulation). These results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans, and highlight the benefits of hybrid models.
Original languageEnglish
Article number1700186
JournalJournal of the Operational Research Society
Volume2021
Issue number3
Early online date3 Feb 2020
DOIs
Publication statusE-pub ahead of print - 3 Feb 2020

Keywords

  • Healthcare
  • decision support system
  • discrete event simulation
  • forecasting
  • integer linear programming

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