Researching the Market for Fraud, Waste and Abuse Detection in Claims Data

December 20, 2013 12:44 pm
Posted By: Stephen Manti

As part of my graduate assistantship at the Center for Health Information and Decision Systems (CHIDS), I have been researching vendor solutions in the market for prevention and detection of Fraud, Waste and Abuse (FWA) in healthcare billing claims, also called anomalous claims.  My analysis is related to a larger effort at CHIDS to evaluate current FWA capabilities and explore future innovations in predictive modeling that may enhance the appropriate detection of anomalous claims.  We are conducting this research program with our industry partner, CNSI, who has developed the ClaimsSure product, and is using innovative techniques and algorithms to combat Medicaid fraud. My analysis may help provide a reference point for the current state of the market and where it is headed.

Prior to business school, I worked with a large health insurance company in the IT function supporting and developing their enrollment and claims systems on a mainframe platform. We were not using any of these FWA prevention tools that I found, and certainly not in any widespread fashion in the systems that I was working on. I was surprised that a company of that size did not have a FWA prevention tool in place. But when I started analyzing other large firms in the health insurance space, it appears that not a lot of them have a well-developed FWA prevention system in place that is leveraging advanced analytics. Most of the companies have some sort of basic fraud detection tool in place to act as a barrier to stop fraudulent claims from being processed, but the use of an advanced real-time FWA prevention tool with a dedicated team working with the solution does not appear common, or maybe they hide away this unit from public disclosures.

The Department of Justice and Health and Human Services has been slowly ramping up the investment in the FWA detection over the last decade. A new report shows that for every dollar spent on health care-related fraud and abuse investigations in the last three years, the government recovered $7.90. In the year 2012 the government recovered $4.2 billion dollars from individuals and companies who attempted to defraud federal health programs. A dedicated Health Care Fraud Prevention and Enforcement Action Team (HEAT) was created in 2009 to prevent FWA in the Medicare and Medicaid programs. (HHS press release)

In June 2011 the Department of Health & Human Services (HHS) and the Centers for Medicare & Medicaid Services (CMS) launched the “Fraud Prevention System,” it takes a method and strategy used by credit card companies by implementing predictive analytics to identify and prevent paying for improper claims. The primary contract for the system was awarded to Northrup Grumman though Verizon, which has a similar system to fight fraud, and IBM also has contracts for various aspects of development and implementation.  CMS and WellPoint are the biggest clients for this system and are reporting benefits of having a FWA prevention and detection tool.  Within the first 6 months of usage, WellPoint opened 90 investigations and achieved $27 million in projected savings. Some of the other larger companies that have developed similar systems are Verisk Health, GDIT, EMDEON, LexisNexis and SAS. The products offered by each of these companies offer to do the job of detecting and preventing fraud through different approaches.

This FWA predictive analytics overall market is still in its nascent stage. These systems are being improved to help detect and prevent fraud better. An important piece of this puzzle could reside with the government.  If the government aggregated and made claims data available with verified fraudulent claims flagged, it would help in the development of innovative models to fight FWA.  Thanks for reading, follow this blog for updates on the FWA research at CHIDS.