Simulation for sustainable health care

The World Commission on Environment and Development (Brundtland Commission) defines sustainable development as ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (WCED, 1987). An ecologically balanced environment, long-term economic well-being and social equity are some of the end goals that are commonly associated with sustainable development and achieving these has become increasingly vital for organisations. Increasing organisational awareness towards a more sustainable future and acquiring the managerial skills to do so are thus critical. One way to achieve this is through the use of decision games called ‘serious games’ in the education process (Katsaliaki and Mustafee, 2013, 2014). The use of computer simulation as a decision support tool is similarly valuable as it enables the stakeholders to experiment with sustainable strategies, which in turn leads to a deeper and more holistic understanding of the sustainable system being modelled.

The focus of this special issue is on modelling and simulation for sustainable development in health care; it seeks to extend the scope of systems’ modelling to incorporate sustainability-related criterion since there is a growing awareness among the organisational stakeholders that their success is profoundly dependent on creating a harmony among the Triple Bottom Line (TBL) of sustainability. TBL refers to the consideration of economic, social and environmental responsibilities with respect to decision making and setting strategic priorities.

A cross-industry review of literature on ‘modelling for sustainability’ suggests that there has been an unequal treatment of economic, social and the environmental factors in studies employing computer simulation (Fakhimi et al, 2013). For example, modelling and simulation studies in non-healthcare topics such as sustainable manufacturing and green supply chain have mostly neglected the social factors pertaining to sustainability. Taking the case of health care, it is a welfare-oriented citizen-centric service and therefore health-care modelling cannot ignore the social factors. The application of simulation in health care has steadily increased especially after the mid-1990s (Katsaliaki and Mustafee, 2011), but there continues to be a dearth of studies which have considered the TBL of sustainability. With the objective of addressing this gap in the literature we invited contributions from academics and practitioners researching conceptual, methodological and technical advances in health-care modelling and, through the targeted call for the special issue, ensured that the submitted work focussed on sustainability; we have been largely successful in this endeavour. We also considered studies that have applied modelling and simulation for practical problem solving and have considered TBL for sustainability analysis. Most of the paper underwent two or more rounds of reviews and total of nine papers were finally accepted. The remainder of the editorial presents a synopsis of these articles and highlights the TBL aspects of the healthcare modelling work that has been done by the authors.

The first paper of the special issue is by Petering et al (2015). It presents research on the impact of intensive care unit (ICU) cost reimbursement policies on an ICU’s ability to satisfy the triple bottom line of sustainability. They use a discrete-event simulation to test two reimbursement policies. The findings suggest that a policy based on patients’ length of stay in ICU generates higher profit compared to a reimbursement policy that is based on total time doctors spend on treating the patients. However, the TBL analyses of the policies show that the former is associated with socially and environmentally unsustainable outcomes whereas the latter policy demonstrates greater adherence to the TBL of sustainability while also yielding a healthy profit.

The paper by Borgman et al (2015) focuses on a problem that is increasingly being experienced in the UK NHS; the overcrowding of emergency departments and consequently the significant number of breaches in the Government’s 4-hour waiting target for A&E. The study is based in the Netherlands where one source of overcrowding is attributed to an increasing number of patients who choose to visit emergency department for acute care outside office hours. The authors seek to transform emergency care by designing an optimal process which merges individual services to provide a single point of access (Integrated Emergency Post). Towards this they develop a discrete-event simulation, the results of which show a sustainable solution to Emergency Department overcrowding, resulting in cost savings for the hospital, increased quality of care for the patients and better usage of resources.

Like the previous paper, the research presented by Patrick et al (2015) is in the context of patient flow and target waiting times; however the emphasis is on long-term care and the need to bridge the gap between acute and community services. The authors present a model that helps determine the capacity requirement for long-term care (authors refer to this as downstream level) which would enable a steady throughput of patients from hospitals to downstream care while still adhering to the waiting time targets for patients accessing such care while still in the community. For sustainable health care it is imperative that sufficient capacity exists and towards this the authors use the model to provide policy recommendations for planning of long-term care.

The next paper by Van Huele and Vanhoucke (2015) is on integrating social and economic measures in the context of operating theatre modelling. Their metrics related to the economic component of sustainability include efficiency and productivity measures like lead time and the number of patients that are treated; examples of social metrics include personal preferences of the physicians. The authors develop a mathematical model to test their integrated approach and measure the aforementioned economic and social factors. The results of the experiments show the positive impact on both throughput and staff preferences and the authors conclude that aligning staff planning with capacity planning would lead to better management of operating theatres.

System Dynamics (SD) modelling adopts a holistic systems perspective and uses stocks, flows and feedback loops to study the behaviour of complex systems over time. The central concept is that change to one part of a system will impact all other parts of an interrelated system. Qualitative System Dynamics relies on system representation through causal loop diagrams. The causal loop diagrams can be transformed into Stock and Flow Diagrams and the resultant SD model can be used to perform a more detailed quantitative analysis. Lyons and Duggan (2015) use both these forms of SD modelling in support of policy analysis for sustainable healthcare. Their whole-system SD approach provides quantitative simulation and qualitative conceptual models for long-term policy analysis, investment and development planning in sustainable health care. The case study establishes the methodological aspects of the SD modelling paradigm that makes it particularly significant for investigating the TBL of sustainability.

Brittin et al (2015) present a study on public health intervention. The purpose of the study is to inform the planning of public health programmes, as also urban planning, which would improve chronic disease outcomes for the community over time. Towards this the authors develop a SD model to simulate chronic disease prevalence and sustainable interventions for a low-income urban community in Chicago, Illinois. The findings show that TBL interventions for addressing social, economic, and environmental determinants of health are essential for reversing the direction of community-level chronic disease trends. The study also demonstrates the value of using SD to evaluate the effects of simulated interventions which would, in turn, make it possible for the public health and the urban planners to make decisions related to resource allocation.

SD is yet again the choice of modelling methodology in research presented by Vanderby et al (2015). The authors use this technique to model the complete continuum of care related to an osteoarthritis care system in the Province of Alberta, Canada. It models patients with osteoarthritis as they transition to various stages of care starting from the onset of the disease through to end-stage care. The SD simulation supports resource planning and policy development by providing insights into the size and characteristics of the patient population, their resource requirements and associated health care costs. The model can be used to explore alternative scenarios to ensure the system is aligned with patients’ needs and the health-care system remains sustainable.

Smith and Harper (2015) focus on the importance of trust for ensuring sustainable operations of health facilities. They consider the build-up of trust as the societal component of sustainability and present a practical application of trust modelling. They take the example of a community health clinic in a rural part of Uttar Pradesh in North India. They use Monte Carlo simulation to model the growth of trust and usage of the mother-and-child clinic with the take-up of services being influenced by spread of positive experiences within the immediate community and wider region. The authors believe that trust modelling can provide a good estimation of the likely use of facilities in regions where information about services and trust are spread by word of mouth.

The final paper of the special issue is by Jean et al (2015). The paper is on predicting the impact of telehealth integration in existing healthcare systems. Telehealth provides remote access to health services and is seen as a possible technology-based intervention that could possibly address some of the challenges associated with the conventional health-care system. The authors have developed a parametric simulation model for this purpose. The model considers demographic changes, changes in resource configuration and economic context affecting the health-care system and predicts implications over time.

We planned the ‘Simulation for Sustainable Healthcare’ special issue with the aim of generating a critical mass of research made possible through the convergence of health-care modelling and simulation and sustainable operations management. Towards this end we have been successful in attracting high quality papers that focused on the holistic and long term view of modelling health-care systems, and with particular emphasis on establishing a harmony between the economic, social and environmental objectives of sustainability. The realisation of the special issue was made possible through the contributions of the authors, the peer reviewers and the members of the JOS editorial board. We would like to thank them all!