Simulation modelling and resource allocation in complex services.

To cite: Bayer S. BMJ Qual Saf 2014;23:353–355. Allocating resources to healthcare services is difficult and often contested—not least in the context of funding shortfalls and rises in demand. Tako and colleagues 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 capacities in different parts of a complex service system comprising multiple resources and pathways. In such situations, having an approach which aids understanding of the relationships between parts of the system and which allows testing different resource allocations and policies in a virtual experiment before committing to specific real investments (and reaping real consequences) constitutes a tremendous boon. Tako et al describe the obesity service as an interconnection of multiple microsystems consisting of individual clinics, ranging from dietary advice and medical treatment to surgery. Of course, this interconnectedness does not represent a unique characteristic of the service they studied—many healthcare services are of this nature. Indeed, some healthcare services that at first appear unconnected and isolated, on more careful consideration prove to be influenced by other services and in turn influence other services. As in the case studied in this paper, feedback between different parts of the care system and bottlenecks along a care pathway can result in one part of the system being affected by investments in another. Their model allows the authors to identify the impacts that changes in patient referral rates and resources (surgeons and physicians) exert on waiting times. For other investments, the relevant resources and outcome variables might differ. However, whether we invest in bed capacity, operating theatre slots or physiotherapy hours, and whether the outcomes we care about are costs, numbers of patients treated or quality indicators, the basic analytical problem remains the same and can benefit from similar simulation modelling approaches to identify solutions. Tako et al employed discrete event simulation, allowing the authors to model individual patients in their journey through the system and account for the EDITORIAL