Using medicare data for comparative effectiveness research: opportunities and challenges

Abstract

Background: With the introduction of Part D drug benefits, Medicare began to collect information on diagnoses, treatments, and clinical events for millions of beneficiaries. These data are a promising resource for comparative effectiveness research (CER) on treatments, benefit designs, and delivery systems.

Objective: To explore the data available for researchers and approaches that could be used to enhance the value of Medicare data for CER. CHALLENGES AND OPPORTUNITIES: Using currently available Medicare data for CER is challenging; as with all administrative data, it is not possible to capture every factor that contributes to prescribing decisions and patients are not randomly assigned to treatments. In addition, Part D plan selection and switching may influence treatment decisions and contribute to selection bias. Exploiting certain program aspects could address these limitations. For example, ongoing changes in Medicare or plan policies and the random assignment of beneficiaries with Part D low-income subsidies into plans with different formularies could yield natural experiments.

Policy implications: Refining policies for time to data release, provision of additional data elements, and linkage with more beneficiary level information would improve the value and usability of these data. Improving the transparency and reproducibility of findings, and potential open access for qualified stakeholders are also important policy considerations. Data needs must be reconciled with current policies and goals.

Conclusions: Medicare data provide a rich resource for CER. Leveraging existing program elements, combined with some administrative changes in data availability, could create large data sets for evaluating treatment patterns, spending, and coverage decisions.