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A decision support system for demand and capacity modelling of an accident and emergency department

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A decision support system for demand and capacity modelling of an accident and emergency department. / Ordu, Muhammed; Demir, Eren; Tofallis, Christopher.

In: Health Systems, 06.01.2019.

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@article{611a3e4cd8bc4b99a0b28bca4dfe6930,
title = "A decision support system for demand and capacity modelling of an accident and emergency department",
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.",
keywords = "Demand and capacity modelling, discrete event simulation, forecasting, accident and emergency department, decision support system, Health care, health care",
author = "Muhammed Ordu and Eren Demir and Christopher Tofallis",
note = "{\textcopyright} 2019 Operational Research Society. ",
year = "2019",
month = jan,
day = "6",
doi = "10.1080/20476965.2018.1561161",
language = "English",
journal = "Health Systems",
issn = "2047-6965",
publisher = "Palgrave Macmillan",

}

RIS

TY - JOUR

T1 - A decision support system for demand and capacity modelling of an accident and emergency department

AU - Ordu, Muhammed

AU - Demir, Eren

AU - Tofallis, Christopher

N1 - © 2019 Operational Research Society.

PY - 2019/1/6

Y1 - 2019/1/6

N2 - 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.

AB - 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.

KW - Demand and capacity modelling

KW - discrete event simulation

KW - forecasting

KW - accident and emergency department

KW - decision support system

KW - Health care

KW - health care

UR - http://www.scopus.com/inward/record.url?scp=85063266136&partnerID=8YFLogxK

U2 - 10.1080/20476965.2018.1561161

DO - 10.1080/20476965.2018.1561161

M3 - Article

JO - Health Systems

JF - Health Systems

SN - 2047-6965

ER -