Modeling tools played an important role in the debate over health care reform legislation–quantifying the likely effects of changes and specifying the extent to which effects will vary by the design of a policy option. Such tools can continue to be useful as the new law is implemented and as additional reforms (e.g., payment reforms) are considered. For models to be useful, however, there must be data to support them and the results must be effectively communicated to policy makers.

Previous researchers (Glied, Remler, and Graft Zivin 2002; Weinstein et al. 2003) have produced guidelines regarding best practices for developing health care policy models and reporting results to stakeholders. In this paper, we focus more narrowly on data required to develop useful models, and the limitations of existing data sources. We begin by reviewing two types of models commonly used to evaluate health policy options: microsimulation and cell-based models. For each type of model, we discuss its basic mechanisms, its applications, the data requirements, and other factors that may affect its scope. We then discuss the limitations of the available data. We conclude with recommendations for enhancing existing databases, and developing additional data inputs that could be useful in modeling health policy outcomes.