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Data · dataset · 2026

Modelling patient flow through hospital emergency departments: a framework for generalisable discrete event simulation modelling using Coxian distributions

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32634096.v1

Hospital emergency departments operate under significant pressure worldwide.

Description

Overcrowding is a frequent occurrence, caused by a combination of high presentation numbers, insufficient resources within emergency departments, and long delays in the transfer or discharge of patients due to a lack of availability of hospital beds and care in the community. Frequent breaches of time-based performance targets are reported in a wide range of national healthcare systems, including the UK and Australia, which are the particular focus of this work.

These issues are projected to intensify, as emergency department numbers continue to climb - in Australia, at a rate of 3.7% annually. Budgetary constraints mean that straightforward solutions, such as increasing resource capacity, are often infeasible. Modelling and simulation techniques can therefore play an essential role in the detection and improvement of patient flow- related issues.

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There is a growing interest in the development of mathematical models which are sufficiently generalisable for use in more than one emergency unit.<br><br>This thesis presents a novel simulation modelling framework for emergency departments, which aims to support model generalisability and reuse. The framework utilises the combined benefits of discrete event simulation and Coxian phase-type distributions to model emergency department patient flow.

Discrete event simulation is used to capture emergency department behaviour over time as an evolution of stochastic variables, and to perform scenario experimentation for process improvement. Coxian phase-type regression models are used to estimate covariate-specific length of stay in the system. The modelling framework is applied to a dataset from an emergency department in Adelaide, South Australia.

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Where it is published

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Inferred from text
Simulation 75%
Provenance · 3 source records, 8 field assertions
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ZivaHuboai:figshare.com:article/3263409613 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/3263409613 d agoJSON v1
DMU Figshareoai:figshare.com:article/3263409613 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].local:field:earth-environmentalmapping · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[field].local:field:earth-environmentalmapping · dro deakin edu auconnector:dro_deakin_edu_au@1.0.0
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