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Big Data Health Care Fraud: Tip of the IceBerg

Big Data Health Care Fraud: Tip of the IceBerg

Just how big is health care fraud? You could measure it in dollars: $70 billion. You could measure it in a percentage of health expenditures: three percent. But just how much is that? Well, think of your local drugstore. Think of all of the health care items they sell there, from cold medicine to toothpaste—everything that you don’t need a prescription to buy. All of those items – the entire industry of retail health products- are roughly equivalent to today’s healthcare fraud: three percent. Seventy billion dollars.

Contrary to popular belief, more than 80 percent of this fraud is not being perpetrated by patients, but fraudulent health care providers. From Texas to New York, and dozens of states in between, organized criminals are setting up false medical clinics, scheduling bogus appointments and filing fraudulent claims, raking in billions of dollars from the health care system. How do you stop it?

Big data offers some promising opportunities. By analyzing all of the disparate data spread across the healthcare system, big data analytics help organizations root out fraud before it happens. This eliminates the ‘pay and chase’ model, instead asking questions such as:

  • Are the beneficiaries or patients that are enrolling who they claim?
  • Have they disclosed all assets, income and the correct state of residence?
  • What are the true backgrounds of the practitioners, officers and agents?
  • What is the risk profile of a provider based on its background and associations?
  • What significant events are occurring between enrollment periods?

At the core of any big data solution is the data itself. In addition to the raw information in the health system, many organizations turn to third-party risk solution providers with access to large public records databases. LexisNexis, for example, provides big data analytics that leverage our 34 billion public records and 36,000 legal, business and news sources. This information is critical to answering the question at the center of the health care system: can I trust this provider?

By bringing tens of thousands of disparate sources together through big data, health care providers can resolve, verify and authenticate identity with 99.9 percent confidence. The results can be surprising. For example, we recently analyzed a Medicaid provider file which contains a list of individuals or businesses registered to receive Medicaid funds in exchange for delivering care to patients. Our analysis found that over one percent of registered providers were deceased, 1.7 percent had either been sanctioned or excluded, 0.5 percent were registered sex offenders, and that the file included incarcerated individuals and those who had undisclosed associations with excluded providers. This kind of analysis draws insights from multiple sources of data to make intelligent information connections that are beyond the obvious.

As health care costs keep growing and fraud becomes an increasing target for organized crime, big data will be critical—but it will not be easy. To fight fraud, companies like LexisNexis have leveraged parallel-processing computing platforms and large scale graph analytics for more than a decade. These developments will continue to advance, but so too must the government organizations responsible for administering health care programs. They will need to develop a comprehensive approach to managing their providers—one that ensures program integrity and fulfills our shared commitment to those with a legitimate health care need.

 

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