How Data Analysis Can Solve the Multi-Billion Dollar Medicare Issue

Learn how this one Medicare issue is affecting taxpayers

Jason RiccioSeptember 14, 2017

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Imagine being overpaid 10% by your employer. If you make $50,000 per year, that’s a nice bonus of $5,000…until finance finds out about the mistake and you have to hand back over the funds. But what if the overpayment is $16 billion? How do you recoup that cash? Do you recoup that cash?

Unfortunately, that’s not a “what if” scenario—that’s the real-life scenario that’s played out throughout the last few years, as Medicare Advantage health plans were mistakenly overpaid $16 billion by the Federal government.  And Basic Medicare also struggles with accurate payments to healthcare providers; with an average 11% error rate, it cost the Federal Government $41 billion in incorrect payments. Yet the Centers for Medicare & Medicaid Services (CMS) has spent $117 million on audits since 2010, only to recoup just under $14million.*

Categories of Billing Mistakes

There are three main categories that contribute to billing mistakes: billing fraud, waste and abuse, and over charges/underpayments. Let’s take a look at the various categories.

Over charges/underpayments:  Once CMS overpays a health plan provider for Medicare Advantage services that money is incredibly difficult to recoup.  CMS often doesn’t realize the overpayment due to a lack of audit standards, and when they do they often collect much less due to resistance from insurers, who often can’t recoup overpayments from the providers who over-charged. To minimize the overpayment, both the dollar amount and frequency, CMS needs better control of their data. Automating data quality, data balancing, data control, and data reconciliation would allow many of these errors to be caught before they were paid.  CMS could take note from the private sector, specifically the telecommunications industry, which leverages these types of data checks to eliminate over and under-billing for their customers.

Waste and abuse: One big source of abuse that CMS could eliminate is the use of deceased people to claim Medicare benefits.  With an automated data control and reconciliation tool, CMS could validate its list of members against the death master list to ensure no benefits are paid-out to the deceased.  Other abuse can be spotted by looking at the data, such as over-ordering of medical supplies.

Fraud:  Fraud poses a slightly different risk, but a risk that can be mitigated. At the core of this abuse is, again, data. By leveraging data analysis and analytics, CMS can identify high-risk cases of Medicare fraud.  Analytics can be used to flag high fraud risk providers to help CMS focus their resources on investigating those providers to help eliminate healthcare provider fraud. Fraud is often due to there being insufficient supporting documentation to determine if the service/procedure was medically necessary.  Again, prior to paying for or approving the procedure, CMS could reconcile the data to match the request with the necessary supporting documents to ensure it is medically necessary, and not fraud.

Mitigating Risk

We have more data available to us now than ever before.  What we do with the data can solve complex problems, or can be the cause of the problems.  In this case, insufficient data analysis, data controls, and data reconciliation is a large factor that goes into these “billing mistakes,” costing tax-payers billions of dollars each year.  With the explosion of big data environments, it is important to ensure that data is accurate and trustworthy, which can then be trusted to help address business issues.

When addressing data issues such as these, solution efficiency is a very important aspect.  Choosing a data solution that can automate these types of checks can save a lot of time and resources, eliminating manual performance of these tasks.  A solution should be flexible enough to read data from any data source, in any data format, and be able to apply rules or logic to that data.

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