The Association of American Medical Colleges' Local Area Physician Workforce Modeling Project.

Physician workforce planning must address multiple concerns such as having sufficient numbers and adequate geographic distribution of physicians and pressures for physicians to adapt to new models of care and payment. Though there are national workforce planning tools, planning tools for local areas have been scarce. This article describes a dynamic simulation model developed as a pilot project to support physician workforce planning in 2 metropolitan areas, Cleveland and Albuquerque (February 2014-June 2016). This model serves as a prototype for planning tools that could be used by medical educators and local health systems to project the effect of different policies on physician supply and demand. System dynamics and group model building approaches were used to develop the model with the participation of local stakeholders to create the model's causal structure. The model included determinants of the demand for primary and specialty care for the local population and projected the effects of births and deaths, aging, level of chronic illness present, and migration on demand. Physician supply was disaggregated by primary versus specialty care, age, sex, and work setting and projected based on completions of local residency programs, physician migration in and out of the area, and retirements. Feedback relationships between supply and demand (e.g., adequacy of care affecting the distribution of chronic illnesses, demand for care influencing in- and out-migration of physicians) were also included and had important effects on the results produced by the model. Scenarios were simulated that projected increased demand for care (e.g., through expanded insurance coverage) and increased supply (e.g., through practice incentives to encourage in-migration) and a combination of these. An expanded advanced practice registered nurse and physician assistant capacity scenario was also simulated. In Albuquerque, the combination scenario yielded the greatest increases in local physician supply.

Combined Regional Investments Could Substantially Enhance Health System Performance And Be Financially Affordable.

Leaders across the United States face a difficult challenge choosing among possible approaches to transform health system performance in their regions. The ReThink Health Dynamics Model simulates how alternative scenarios could unfold through 2040. This article compares the likely consequences if four interventions were enacted in layered combinations in a prototypical midsize US city. We estimated the effects of efforts to deliver higher-value care; reinvest savings and expand global payment; enable healthier behaviors; and expand socioeconomic opportunities. Results suggest that there may be an effective and affordable way to unlock much greater health and economic potential, ultimately reducing severe illness by 20 percent, lowering health care costs by 14 percent, and improving economic productivity by 9 percent. This would require combined investments in clinical and population-level initiatives, coupled with financial agreements that reduce incentives for costly care and reinvest a share of the savings to ensure adequate long-term financing.

Understanding social forces involved in diabetes outcomes: a systems science approach to quality-of-life research.

PURPOSE: The field of quality-of-life (QOL) research would benefit from learning about and integrating systems science approaches that model how social forces interact dynamically with health and affect the course of chronic illnesses. Our purpose is to describe the systems science mindset and to illustrate the utility of a system dynamics approach to promoting QOL research in chronic disease, using diabetes as an example. METHODS: We build a series of causal loop diagrams incrementally, introducing new variables and their dynamic relationships at each stage. RESULTS: These causal loop diagrams demonstrate how a common set of relationships among these variables can generate different disease and QOL trajectories for people with diabetes and also lead to a consideration of non-clinical (psychosocial and behavioral) factors that can have implications for program design and policy formulation. CONCLUSIONS: The policy implications of the causal loop diagrams are discussed, and empirical next steps to validate the diagrams and quantify the relationships are described.

A simulation model for designing effective interventions in early childhood caries.

INTRODUCTION: Early childhood caries (ECC)--tooth decay among children younger than 6 years--is prevalent and consequential, affecting nearly half of US 5-year-olds, despite being highly preventable. Various interventions have been explored to limit caries activity leading to cavities, but little is known about the long-term effects and costs of these interventions. We developed a system dynamics model to determine which interventions, singly and in combination, could have the greatest effect in reducing caries experience and cost in a population of children aged birth to 5 years. METHODS: System dynamics is a computer simulation technique useful to policy makers in choosing the most appropriate interventions for their populations. This study of Colorado preschool children models 6 categories of ECC intervention--applying fluorides, limiting cariogenic bacterial transmission from mothers to children, using xylitol directly with children, clinical treatment, motivational interviewing, and combinations of these--to compare their relative effect and cost. RESULTS: The model projects 10-year intervention costs ranging from $6 million to $245 million and relative reductions in cavity prevalence ranging from none to 79.1% from the baseline. Interventions targeting the youngest children take 2 to 4 years longer to affect the entire population of preschool-age children but ultimately exert a greater benefit in reducing ECC; interventions targeting the highest-risk children provide the greatest return on investment, and combined interventions that target ECC at several stages of its natural history have the greatest potential for cavity reduction. Some interventions save more in dental repair than their cost; all produce substantial reductions in repair cost. CONCLUSION: By using data relevant to any geographic area, this system model can provide policy makers with information to maximize the return on public health and clinical care investments.

Using system dynamics modeling to understand the impact of social change initiatives.

Community psychologists have a long history of interest in understanding social systems and how to bring about enduring positive change in these systems. However, the methods that community psychologists use to anticipate and evaluate the changes that result from system change efforts are less well developed. In the current paper, we introduce readers to system dynamics modeling, an action research approach to studying complex systems and the consequences of system change. We illustrate this approach by describing a system dynamics model of educational reform. We provide readers with an introduction to system dynamics modeling, as well as describe the strengths and limitations of the approach for application to community psychology.

System dynamics modeling for public health: background and opportunities.

The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy.