Executive Summary
Modeling Health Care Policy Alternatives
We review three types of computational models commonly used to evaluate health care
policy alternatives: microsimulation, system dynamics, and spreadsheet models. For each
type of model we discuss its basic mechanisms, data requirements and other factors that
may affect its scope, and the domain of application. We also discuss how to build better
models and perform analyses that are currently out-of-scope by changing the type of data
that are currently collected and access to those data. To improve modeling capabilities,
we recommend fielding a health-focused, nationally representative, longitudinal study on
individuals that would allow researchers to better understand decision-making
surrounding health insurance take-up, as well as relationships among insurance status,
health care access, and health outcomes. We also suggest improving access to linked data
sources, such as employee-employer linked data, and developing more data sources
focused on providers. In addition to data collection, we recommend that funding be
provided to identify causal parameters and to clarify ranges of effects that have been
reported in the literature. Finally, we suggest establishing a forum for routine
communication between modelers and policymakers to ensure that model results are fully
understood and can be applied effectively.