How big data helped uncover the largest Medicare fraud takedown

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Data analytics played a key role in the Department of Justice's Medicare fraud sweep that charged 301 individuals who allegedly participated in Medicare fraud schemes involving $900 million in false billings. However, the data systems also make the government payer a target, according to Marketplace.

On June 22, the DOJ reported the nationwide sweep led by the Medicare Fraud Strike Force. Medicare Fraud Control units from 23 states participated in the arrests. It was the largest crackdown on Medicare fraud in history, both in terms of how many defendants were charged and the total of fraudulent billings.

Ann Maxwell, assistant inspector general for evaluations at the Office of Inspector General, told Marketplace the agency is getting better at finding information to charge individuals because it has more access to data and the tools to analyze that data.

The OIG now has access to real-time Medicare and Medicaid data, according to Marketplace. And, in 2014, CMS formed the Office of Enterprise Data and Analytics and named Niall Brennan the agency's first ever chief data officer. CMS also plans to add 10 employees focused on data analysis this year.

"I think the thing that really is different is the speed with which we were able to do our jobs," Ms. Maxwell told Marketplace. "In the past when we didn't have the kind of computing capability that we do now, trying to determine a national scope could take weeks, months, sometimes even years. Now we are talking about a couple of hours."

However, Malcolm Sparrow, a professor at Harvard Kennedy School of Government, told Marketplace the automated billing systems that process Medicare and Medicaid claims remain an attractive target for hackers since the systems are automated and there is little to no human involvement. 

"These are beautiful targets from a criminal's perspective," Mr. Sparrow said in the report. "You can test the system and then you can just submit tens of thousands of claims, absolutely sure because it's automated that they are all going to be paid."  

More articles on big data:

Improving hospital efficiency through data driven analytics 
Combatting infections before they occur with real-time and predictive analytics 
Mining for data gold: Transforming healthcare with natural language processing 

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