TY - JOUR
T1 - A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach
AU - Ordu, Muhammed
AU - Demir, Eren
AU - Tofallis, Chris
AU - Gunal, Murat
N1 - © 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an accepted manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 03 Feb 2020, available online: https://doi.org/10.1080/01605682.2019.1700186.
PY - 2020/2/3
Y1 - 2020/2/3
N2 - 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.
AB - 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.
KW - Healthcare
KW - decision support system
KW - discrete event simulation
KW - forecasting
KW - integer linear programming
UR - http://www.scopus.com/inward/record.url?scp=85078948647&partnerID=8YFLogxK
U2 - 10.1080/01605682.2019.1700186
DO - 10.1080/01605682.2019.1700186
M3 - Article
SN - 0160-5682
VL - 2021
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
IS - 3
M1 - 1700186
ER -