@article{ref1,
title="A decision support system for demand and capacity modelling of an accident and emergency department",
journal="Health systems (Basingstoke, England)",
year="2020",
author="Ordu, Muhammed and Demir, Eren and Tofallis, Chris",
volume="9",
number="1",
pages="31-56",
abstract="Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 - January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.<br><br>© Operational Research Society 2019.<p /> <p>Language: en</p>",
language="en",
issn="2047-6965",
doi="10.1080/20476965.2018.1561161",
url="http://dx.doi.org/10.1080/20476965.2018.1561161"
}