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Simulation modelling and resource allocation in complex services
  1. Steffen Bayer
  1. Correspondence to Dr Steffen Bayer, Program in Health Services & Systems Research, Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore 169857, Singapore; steffen.bayer{at}

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Allocating resources to healthcare services is difficult and often contested—not least in the context of funding shortfalls and rises in demand. Tako and colleagues1 report on the use of simulation modelling to plan capacity investments in a service which is expecting a significant increase in demand. The obesity service in the English hospital they studied required an investment to manage waiting times and meet future anticipated demand. The authors show how simulation modelling can be used to balance investments between different parts of a service and to deal with the uncertainty inherent in planning in a healthcare setting. While their work concerns an obesity service, the need to balance capacities (and therefore investments) will be familiar to many healthcare practitioners across diverse clinical domains. Their simulation shows how investments in capacities in different parts of the service as well as measures to manage demand can be balanced in the specific case of the service they were investigating.

For most readers the conclusions that Tako et al draw for the particular service they studied might be of less relevance than the modelling approach they have taken to arrive at these conclusions. Healthcare managers are frequently required to devise an allocation of resources—to meet rising demand while meeting acceptable performance standards, as in the paper by Tako et al, or to achieve other improvements in efficiency, effectiveness or patient experience. In clinical medicine, new treatments enter practice after trials establish their efficacy and safety. Before introducing new service innovations, one would hope that, at a minimum, a pilot study would examine the consequences of the service innovation.

When allocating resources, we would similarly desire a safe and cost-effective way to examine the possible consequences of the investment in advance. Even experienced managers might struggle to predict the consequences of increasing …

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