There’s been a noticeable shift in the way the federal government uses data to detect fraud hot spots and target specific providers or pockets of healthcare fraud over the last several years. For prosecutors, the use of real-time data has revolutionized the approach to fraud cases.
But the feds are still pursuing the ultimate goal of using predictive analytics to prevent fraudulent payments rather than chasing down fraudulent providers. In some ways they are making progress. The Centers for Medicare & Medicaid Services claims it saved $42 billion over the last two years with increased fraud enforcement, predictive analytics, and better provider screening and enrollment efforts (although that figure may be overblown).
The CMS has invested heavily in its predictive analytics through the Fraud Prevention System (FPS), which saved more than $1.5 billion since it was launched in 2011. However, lawmakers have complained that the CMS still isn’t doing enough to prevent improper payments and have requested detailed information about how many fraud cases are referred through the FPS.
At the same time, Medicare contractors are leaning heavily on predictive analytics to root out fraud, while investigators have emphasized a two-pronged approach to fraud enforcement that includes both data analytics and traditional investigative fieldwork.