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Predictive analytics helps fraud fighters detect sophisticated schemes [Special Report]

Flexibility, human expertise and data integrity are key elements in using data to prevent healthcare fraud

Predictive analytics might be the hottest buzzword in healthcare. Over the last several months alone, data analytics have helped researchers predict where and when the flu is at its peak, allowed providers to determine what kind of care patients will need six months from now, and helped payers identify gaps in care.

Now, predictive analytics is changing the way payers identify instances of fraud, waste and abuse in healthcare. Last month, the assistant inspector general and chief data officer at the Office of Inspector General, Caryl Brzymialkiewicz, highlighted analytics as a key tool in the largest healthcare fraud bust in U.S. history.

Increasingly, both public and private payers are turning to data analytics to identify high risk fraud trends, Andrew Asher, senior fellow and director of data analytics at Mathematica, said in an exclusive interview with FierceHealthPayer: AntiFraud. However, payers are in the early stages of using healthcare claims data to accurately predict fraud schemes. In many ways, fraud fighters are still transitioning from the old "pay-and-chase" models to a more proactive approach, in part because predictive analytics systems are still finding a foothold in the fraud-prevention world.

"Payers remain concerned about the risks of false positives and about the accuracy of models to identify actionable findings," Asher said.

Although predictive analytics is still in its infancy, health insurers are finding notable success in an approach that blends the human element of fraud expertise with customizable models that can rapidly identify sophisticated, high-risk fraud schemes.