Health Economics and Financing

bottle of pills on money

RAND Health conducts a variety of studies on medical costs, insurance coverage, reimbursement models, and consumer health expenses—including proposed health care reforms. Our research identifies opportunities to control spending while improving health care access, efficiency, and outcomes.

From the RAND Blog

  • The Great Patient Experience Survey Myth

    Dec 10, 2014

    Patient experience with care is an essential element in any assessment of health care quality. Surveys give patients a voice and provide fair and relevant indicators that complement other metrics of health care quality to inform patients' choices and providers' decisions about how to improve care.

  • A Case for Exchange-Based, Long-Term Health Insurance Policies

    Nov 10, 2014

    Multi-year health plans have the potential to finally align the interests of health plan enrollees and insurers by reaping long-term benefits of patient health management.

Latest Research and Publications

  • More Daring Ideas to Form Health Policy: Why Not?

    When it comes to health policy, there are two basic approaches: (1) cautious and careful, or (2) disruptive and daring. The former is less threatening, but what might happen if decision makers were more driven by creativity and less concerned about regulations?

  • Predictive Modeling Informs CMS Physician Fee Schedules

    A predictive model provides insight into the values that the Centers for Medicare and Medicaid Services (CMS) use to determine physician payment. Although the model has limitations, it could help support transparency and consistency in the CMS's valuations.

  • Predictive Model Could Apply to Medicare Physician Fee Schedule

    The Centers for Medicare and Medicaid Services uses a resource-based relative value (RBRV) scale to calculate payments for physicians. The values of the scale were validated with predictive modeling, and the result may be helpful in two key applications: flagging codes as potentially misvalued and determining why a code is valued differently than predicted.

Research in Progress

Last updated: August 2013