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Modelling patient choice in healthcare systems: development and application of a discrete event simulation with agent-based decision making

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Journal of Simulation

Abstract

This paper describes the development and application of a simulation tool for modelling patient choice in healthcare systems. Patient choice is already offered in the English National Health Service (NHS) and proposed reforms will propel choice to the forefront of NHS re-structuring by offering it at all levels of treatment. A simulation model is proposed to study the effects of patient behaviour. Although built using discrete event simulation, a patient’s choice between hospitals is not governed by some probability distribution but by the individual decision made by each agent upon observation of the system. Use of the model is demonstrated using data on elective knee operations across Wales. The data provided allows for two separate aspects to be considered. Firstly, model experimentation is used to confirm game theoretical results; namely that increasing choice can actually result in an increase of the average patient waiting time across the entire health system. Secondly, we demonstrate how the model could be used by decision makers to improve overall system performance, and furthermore for solving location–allocation problems. Owing to its generic structure, the tool although demonstrated for the NHS, could be used in any healthcare setting and indeed other sectors where choice is relevant.

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Acknowledgements

Part funding for this project was gratefully received from a Cardiff Undergraduate Research Opportunities Programme award. The authors would also like to acknowledge the time and effort provided by Professor Paul Harper, whose suggestions greatly improved this manuscript. The comments of the various anonymous referees were gratefully received and also added value.

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Correspondence to V A Knight.

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Knight, V., Williams, J. & Reynolds, I. Modelling patient choice in healthcare systems: development and application of a discrete event simulation with agent-based decision making. J Simulation 6, 92–102 (2012). https://doi.org/10.1057/jos.2011.21

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  • DOI: https://doi.org/10.1057/jos.2011.21

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