Fighting Health Care Fraud with Analytics and Intuition

Fighting Health Care Fraud with Analytics and Intuition

Every piece of information helps in the quest to stop Medicare and Medicaid fraud

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Health care fraud costs the US tens of billions of dollars every year, according to the National Health Care Anti-Fraud Association. It also exacts a heavy toll on the people who receive sub-par treatment, no treatment at all or suffer irreparable damage as a result of it.

Stopping fraud is a key goal of private health insurers as well as federal and state governments, and at the NHCAA Institute for Health Care Fraud Prevention Annual Training Conference this year I got a chance to meet some of the people who make this goal a huge priority. Fortunately, these dedicated people have some powerful tools to help them in their quest.

Data Mining for Fraud

Suzette Long, Consulting Manager with Thomson Reuters, is a former dental health professional whose career is now focused on rooting out medical fraud. She and others in her field use sophisticated programs to identify possible fraud cases and research people and schemes. One such tool, the J-SURS fraud and abuse detection system, is used to look at data and identify unusual billing and patterns of behavior as well as perform quick reviews or detailed assessments of suspicious situations. Another tool in the fraud detection arsenal is CLEAR for health care fraud, which we blogged about earlier this year.

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Long is passionate about reducing health care fraud and bringing the thieves to justice. “I’m not looking for a dentist who bills one wrong claim,” she says, “and I’m not looking to take people’s livelihood away. I’m just trying to maintain the fiscal integrity of these programs so that people who need these services can get them. And if we have people robbing it blind, they can’t.”

The Right Brains on the Job

The use of medical practitioners in the fight against fraud makes a lot of sense. “If you give a data manager a set of data, he might be able to look for trends and do predictive modeling,” says Long. “But when you give a nurse the same set of data, she can use medical knowledge and expertise to look for things that don’t look normal.”

Sometimes this makes the job pretty straightforward. “We recovered $1.6 million from one scam,” says Long. This one was quick to spot using the J-SURS system to flag doctors who had unusually high billings for a particular service.

Low-Hanging Fruit

In this case, doctors working in hospitals were billing for complete x-rays without using the professional modifier. (Adding the modifier indicates that they only read the x-rays, as opposed to taking the x-rays as well.) Long’s team surmised that since the doctors were performing the services inside a hospital that had an x-ray department, it was unlikely that they were bringing in their own portable x-ray machines. The team was right, and was able to recover a substantial chunk of stolen money.

Long calls these cases the “low-hanging fruit – things we know are recoverable”. It makes sense to chase these ones since the software makes them easy to find and the obvious discrepancies make them easy to prove. It’s a way to get money back into the system quickly and easily to keep the services available for those who really need them.

Long has heard it all. Some providers rationalize fraud by the fact that the system is blindly paying the fraudulent claims. Some service providers have told her that because Medicaid payments are low, in order for them to make money they have to bill for more services than they perform.

“We quickly have to remind them that every time they sign a claim form, they are saying that what they’ve submitted is truthful and honest. You can’t lie just to increase your reimbursement.”