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Leskovar, Robert; Mariboru, Fakulteta za organizacijske vede Univerza; Баггиа, Аленка; Vukovič, Goran

doi:10.18690/978-961-286-020-2.40

Cited by 1 publication

(1 citation statement)

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“…The use of discrete-event simulation to model hospital departments is well documented in the literature (Jun, Jacobson and Swisher, 1999;Anderson and Merode, 2007;Gunal and Pidd, 2010). Studies include making strategic decisions for various departments (Ballard and Kuhl, 2006;Denton et al, 2006;Vanberkel and Blake, 2007;Leskovar et al, 2011); estimating capacity levels and measuring waiting times (Werker et al, 2009); analysing patient flows (Brenner et al, 2010;Zeng et al, 2012); measuring policy impact (Fletcher et al, 2007); and simulating patient scheduling and utilisations (Harper and Gamlin, 2003;Werker et al, 2009;Lee et al, 2013;Quevedo and Chapilliquén, 2014). It has been argued that the extensive use of process modelling is limited in healthcare compared with other industries (Harper and Pitt, 2004) due to the complexity of the processes and the vast amounts of data required to provide accurate models (Antonacci et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Maximising Patient Throughput using Discrete-event Simulation

Greenroyd

Hayward

Price

et al. 2017

Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis. 204

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