Previously published as: Journal of Management in Medicine
Online from: 2003
Subject Area: Health Care Management/Healthcare
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|Title:||Meeting the four-hour deadline in an A&E department|
|Author(s):||Julie Eatock, (Department of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK), Malcolm Clarke, (Department of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK), Claire Picton, (A&E Department, Hillingdon Hospital, Uxbridge, UK), Terry Young, (Department of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK)|
|Citation:||Julie Eatock, Malcolm Clarke, Claire Picton, Terry Young, (2011) "Meeting the four-hour deadline in an A&E department", Journal of Health Organization and Management, Vol. 25 Iss: 6, pp.606 - 624|
|Keywords:||A&E departments, Coping strategies, Emergency departments, Four-hour operational standard, Hospitals, Length of stay, Patients, Queuing time, Simulation, United Kingdom|
|Article type:||Case study|
|DOI:||10.1108/14777261111178510 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The authors would like to thank the staff at Hillingdon A&E Department, in particular Alaganandan Sivakumar, Martin Sweatman and Matt Kybert, for their assistance and their insightful comments throughout this process.|
Purpose – Accident and emergency (A&E) departments experience a secondary peak in patient length of stay (LoS) at around four hours, caused by the coping strategies used to meet the operational standards imposed by government. The aim of this paper is to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department.
Design/methodology/approach – A discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4,150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data were compared with the corresponding records. Expert opinion was used to generate the pathways and model the decision-making processes.
Findings – The authors were able to replicate accurately the LoS distribution for the hospital. The model was then applied to a second configuration that had been trialled there; again, the results also reflected the experiences of the hospital.
Practical implications – This demonstrates that the coping strategies, such as re-prioritising patients based on current length of time in the department, employed in A&E departments have an impact on LoS of patients and therefore need to be considered when building predictive models if confidence in the results is to be justified.
Originality/value – As far as the authors are aware this is the first time that these coping strategies have been included within a simulation model, and therefore the first time that the peak around the four hours has been analysed so accurately using a model.
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