Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/267668 
Year of Publication: 
2019
Citation: 
[Journal:] Journal of Industrial Engineering International [ISSN:] 2251-712X [Volume:] 15 [Issue:] S1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2019 [Pages:] 253-266
Publisher: 
Springer, Heidelberg
Abstract: 
Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide process targets such as the 'four-hour rule' intended to deal with access blocks. In addition, EDs have introduced various strategies such as 'fast tracking', 'enhanced triage' and new models of care such as introducing nurse practitioners aimed at improving throughput. However, most of these practices require additional resources. Some researchers attempted to optimise the resources using various optimisation models to ensure best utilisation of resources to improve patient flow. However, not all modelling approaches are suitable for all situations and there is no critical review of optimisation models used in hospital EDs. The aim of this article is to review various analytical models utilised to optimise ED resources for improved patient flow and highlight benefits and limitations of these models. A range of modelling techniques including agent-based modelling and simulation, discrete-event simulation, queuing models, simulation optimisation and mathematical modelling have been reviewed. The analysis revealed that every modelling approach and optimisation technique has some advantages and disadvantages and their application is also guided by the objectives. The complexity, interrelationships and variability of ED-related variables make the application of standard modelling techniques difficult. However, these models can be used to identify sources of flow obstruction and to identify areas where investments in additional resources are likely to have most benefit.
Subjects: 
Optimisation
Emergency departments
Patient fow
Simulation
DES
ABMS
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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