Raising the Profile of Simulation and Modelling in Health Services Planning and Implementation

Abstract

Health care systems in major developed countries need to meet an expanding demand for services while simultaneously controlling rising costs, improving quality and increasing productivity. New solutions may require system redesign, involving novel combinations of technology, services and infrastructure. These combinations and their potential benefits may be hard to identify and implement. Diverse stakeholders will need to be coordinated and accommodated. Their effects often occur at multiple levels across public policy and other domains, and over different timescales; unexpected behaviour is to be expected. Evidence for the potential benefits of new service models is important for their widespread acceptance and mainstream adoption. But gathering such evidence where complex service and technological innovations are involved is often hard. Pilot projects may provide some insight into the impact of an innovation, but these often fail to give good feedback on real-life situations where resources may be more constrained. The implications of an innovation for its mainstream use are therefore often difficult to judge. Better methods that can identify and link the context, process, costs and outcomes of potential health and social care innovations at different system levels are needed. Modelling and simulation can achieve this by allowing experimentation with different courses of action in a safe, quick and cheap way. This can help to support health services planning by tempering perspectives which overestimate the reliability of prediction and by bringing uncertainty into the open. Decision-making processes which provide some degree of structure and rationality ‐ but at the same time highlight uncertainties, encourage stakeholder dialogue, and support and document the decision ‐ are desirable. Simulation models have been developed for health care planning since the mid 1960s. 1,2 Among the different approaches, system dynamics 3‐6 and discrete event simulation 7‐10 are particularly prominent. Other approaches include Monte Carlo simulation and agentbased modelling. Such models can be used in a variety of ways. They can be used as a close replica of the real world to explore the potential consequences of decisions under differing assumptions. They can help to facilitate communication between diverse stakeholders by creating a shared representation of the whole system. 11 This can be especially important where understanding has to be created across organizational and professional boundaries. 12 In recent years, powerful computers, more accessible software and growing capabilities in computer graphics and animation have made the conditions more favourable for spreading the use of simulation modelling. Yet despite the proliferation of papers in the academic literature, and individual success stories, there are still major issues around getting these models widely accepted and used as part of mainstream decision making by clinicians, health managers and policymakers. In the UK, initiatives such as making simulation modelling tools widely available in the through the Institute for Innovation and Improvement’s ‘scenario generator’ (www.institute.nhs.uk/scenariogenerator/ general/what_is_the_scenario_generator.html) or the attempt to bring health modellers from academia, NHS and industry together (MASHnet - http://mashnet. info/) are important steps. But frequently calls for the use of modelling fail to result in wider adoption.