摘要：Proposals to improve the US health system are commonly supported by models that have only a few variables and overlook certain processes that may delay, dilute, or defeat intervention effects. We use an evidence-based dynamic simulation model with a broad national scope to analyze 5 policy proposals. Our results suggest that expanding insurance coverage and improving health care quality would likely improve health status but would also raise costs and worsen health inequity, whereas a strategy that also strengthens primary care capacity and emphasizes health protection would improve health status, reduce inequities, and lower costs. A software interface allows diverse stakeholders to interact with the model through a policy simulation game called HealthBound. The multiple shortcomings of the US health system are well known. US health care spending per capita is the highest in the world, but Americans have comparatively high rates of morbidity and premature mortality, 1 along with persistent disparities among subgroups. People with lower socioeconomic status, for example, are much more likely to develop disease and injury and to become disabled or die prematurely as a result, in part because they face greater health threats and are also less likely to have access to high-quality health care. 2 , 3 Various theories have been offered to explain why the US health system performs so poorly and is so costly. 4 , 5 Many point to the lack of health insurance for millions as the system's chief problem. 6 Some criticize the medical industry and the public at large for overemphasizing disease detection and treatment while missing opportunities to reduce preventable risk and protect people's health. 7 , 8 Others blame perverse incentives and community norms that encourage physician entrepreneurship and profit making over collaboration, coordination, or conservative practice. 9 , 10 Still others say that there are too few primary care providers or that the providers we do have are underpaid and unable to offer the highest-quality care. 11 , 12 And some fault private insurers, who pass along high overhead costs to consumers, are unwilling to reimburse adequately for preventive care, and offer a confusing array of coverage plans, creating a substantial administrative burden for providers. 13 Likewise, reform proposals vary widely in their goals and policy levers. One leading proposal calls for wider health insurance coverage and better quality of care through computerization and payment incentives for providers. Supporters claim that these 2 changes will improve people's health and reduce health care costs, effectively remaking the health delivery system. 14 Other proposals run the gamut, from outlining 15 ways to cut costs 15 to focusing on relatively inexpensive population-based programs to increase physical activity, improve nutrition, and prevent tobacco use. 16 Such proposals are usually justified by calculating their costs and benefits over time. However, the mathematical models commonly used to generate these projections typically examine only a small number of variables over a relatively short time, rather than portraying the health system as the diverse and dynamic enterprise that it is. 17 In particular, these models usually ignore the effects of accumulations, time delays, resource constraints, and behavioral feedback. System scientists have shown how such processes must be considered to reach correct conclusions about the net impacts of interventions in systems with many interacting actors, multiple goals, and conflicting interests. 18 – 20 They have also challenged the methodological convention of using artificially short time frames for prospective evaluation, such as when studying investments in youth or interventions to prevent slow-moving chronic diseases. 21 , 22 We present the results of simulations of several intervention scenarios using a computer model that accounts for many factors that make the US health system hard to understand and difficult to change. The model we developed has a relatively long time horizon (25 years) and a causal structure that is broad in scope and rich in realistic detail. Still, the model is sufficiently streamlined to allow extensive testing and analysis. Interventions are represented at a general level without detailing how they might be implemented. Such simplification allowed us to evaluate various intervention strategies and study their fundamentally different impacts on the health system before considering tactical details. Strategic modeling of this sort is not intended to predict the future but rather to help us learn how the complex US health system tends to respond over time to different reform strategies. It supports critical thinking and more robust conclusions about how selected intervention options are likely—in directional terms and relative to one another—to affect performance of the health system in the short and long term. Sterman presented a rationale for using simulation models in this way, i.e., as tools to improve understanding of complex systems. 20 Planners may use our model to simulate many types of interventions, but just 5 scenarios are explored here, starting with a strategy to expand insurance coverage and improve the quality of health care. Four additional strategies are then tested to determine whether alternative approaches might yield better results.
