Modeling health care policy alternatives.
BACKGROUND
Computer models played an important role in the health care reform debate, and they will continue to be used during implementation. However, current models are limited by inputs, including available data. Aim. We review microsimulation and cell-based models. For each type of model, we discuss data requirements and other factors that may affect its scope. We also discuss how to improve models by changing data collection and data access procedures.


METHODS
We review the modeling literature, documentation on existing models, and data resources available to modelers. Results. Even with limitations, models can be a useful resource. However, limitations must be clearly communicated. Modeling approaches could be improved by enhancing existing longitudinal data, improving access to linked data, and developing data focused on health care providers.


CONCLUSIONS
Longitudinal datasets could be improved by standardizing questions across surveys or by fielding supplemental panels. Funding could be provided to identify causal parameters and to clarify ranges of effects reported in the literature. Finally, a forum for routine communication between modelers and policy makers could be established.


CONCLUSIONS
Modeling can provide useful information for health care policy makers. Thus, investing in tools to improve modeling capabilities should be a high priority.
RAND Corporation, 1776 Main Street, PO Box 2138, Santa Monica, CA 90407, USA. ringel@rand.org
Jeanne S
JS
Ringel
Christine
C
Eibner
Federico
F
Girosi
Amado
A
Cordova
Elizabeth A
EA
McGlynn
D003198:Computer Simulation; D003625:Data Collection / Q000379:methods / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D018166:Health Care Reform / Q000706:statistics & numerical data; D006291:Health Policy; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D017146:Models, Organizational; D010043:Outcome and Process Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D013223:Statistics as Topic