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Modeling Health Care Policy Alternatives
Author(s) -
Ringel Jeanne S.,
Eibner Christine,
Girosi Federico,
Cordova Amado,
McGlynn Elizabeth A.
Publication year - 2010
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2010.01146.x
Subject(s) - computer science , documentation , scope (computer science) , health care , data science , data collection , management science , resource (disambiguation) , risk analysis (engineering) , process management , medicine , business , engineering , economics , economic growth , computer network , statistics , mathematics , programming language
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. Materials and 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. Discussion. 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. Conclusion. Modeling can provide useful information for health care policy makers. Thus, investing in tools to improve modeling capabilities should be a high priority.