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A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach. / Ordu, Muhammed; Demir, Eren; Tofallis, Chris; Gunal, Murat.

In: Journal of the Operational Research Society, Vol. 2020, 1700186, 03.02.2020.

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@article{cb9ebe427fae4267a08227ecb113b286,
title = "A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach",
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.",
keywords = "Healthcare, decision support system, discrete event simulation, forecasting, integer linear programming",
author = "Muhammed Ordu and Eren Demir and Chris Tofallis and Murat Gunal",
note = "{\textcopyright} 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.",
year = "2020",
month = feb,
day = "3",
doi = "10.1080/01605682.2019.1700186",
language = "English",
volume = "2020",
journal = "Journal of the Operational Research Society",
issn = "0160-5682",
publisher = "Palgrave Macmillan Ltd.",

}

RIS

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

VL - 2020

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

M1 - 1700186

ER -