The federal and state governments pay for nearly half of all health care in the US. Your tax dollars at work! Guess what you’re not getting for those dollars: a lot of data that researchers might use to assess how the health system functions. As a researcher and as a taxpayer, this really bothers me.
With no way to validate the assertions of [Massachusetts] insurers or regulators, the public–those that actually fund the low-income insurance subsidies–can’t know the extent to which insurers are exaggerating the level of adverse selection or if regulators are unfairly dismissing it. Since Massachusetts’ health reform is so similar to that which will be implemented nationally under the new health reform law, it is a tremendous loss that we don’t know more about precisely how it is working (or not).
A similar lack of data prevents researchers from fully analyzing the risk selection experience of private Medicare plans. There is indirect and self-reported evidence that Medicare Advantage plans experience favorable selection (enrollees healthier than expected). Because plans are not required to release all utilization data the issue cannot be analyzed head-on. As a consequence, the perennial clashes over payment between the industry and budget-conscious politicians and watchdog groups occur in a climate of incomplete information. Medicare Advantage plans are overpaid, but by how much? Taxpayers can’t fully know.
At the heart of these conflicts are empirical questions: Are payments sufficient to cover costs? Are taxpayers getting a fair deal, a bad deal or a bargain? Not surprisingly, insurers and regulators generally provide opposing answers.
How can we tell who is right? With the necessary data so closely guarded by insurers and not available to the public, academics and consumer and taxpayer advocacy groups, who might provide valuable checks of claims made by insurers and regulators, are locked out of the debate.
The hidden data imposes a hidden cost on taxpayers. Though it can’t be fully quantified, there is a likely cost in the form of inflated payments. Insurance companies know far more about their costs and enrollee characteristics than regulators or academics. The information asymmetry plays to their advantage and can only drive up taxpayer costs. Unless plans are compelled to provide data, their advantage is likely to continue as the public begins paying a substantial sum to subsidize exchange-based coverage in every state.
The American tradition of public financing of private coverage is alive and well, and so is the practice of hiding the facts about what taxpayers are getting for their money. Wouldn’t you like to know what you’re paying for? Me too.
So, kudos to Vicki Fung, Richard Brand, Joseph Newhouse, and John Hsu for suggesting what Medicare could do to improve health data access for researchers. Some of their recommendations are:
- Release data faster. The current, roughly 21-month lag time between provision of care and availability of data is too long.
- Release sufficient data to identify all options available to beneficiaries. Choices matter. If you don’t know all available plans and their characteristics you can’t really understand why a beneficiary may have selected the plan he or she did.
- Release Medicare Advantage data. Currently we know far less about MA than about traditional Medicare. Yet we pay a lot to private plans for their participation in MA. It’d be nice to know what we’re getting for that money.
Those would be a step in the right direction, though we could use more transparency health system wide, not just from Medicare.
The paper is a general introduction to Medicare data and how it might be used in comparative effectiveness research. Here’s the 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.