As Medical Provider Fraud Evolves so Should the Investigation

Healthcare fraud related financial losses amount to tens of billions of dollars each year, according to the National Health Care Anti-Fraud Association.

As medical claims fraud in the property/casualty sector has evolved so too has the process to uncover it, says Todd Fannin, director of claims for LexisNexis Risk Solutions. Just last year, the property/casualty industry spent a little over $48 billion on injury claims.

“If just 10 percent of that was actually connected with some kind of fraud, waste and abuse from a medical standpoint, that’s $4 billion plus exposure to the industry,” Fannin says.

Cross-industry fraud – where healthcare fraud, property/casualty and workers’ compensation fraud intersect – is the next step in identifying potentially bad providers.
LexisNexis completed a study that revealed an overlap between providers who commit fraud on the healthcare side and those who do it on the property/casualty side. In addition, there was a greater probability that those providers would be involved in identity theft, tax evasion as well as other criminal activity.

“There was about a 22 percent overlap of seeing the same doctors generally practicing on both the healthcare side and the auto side,” says Fannin. “Within that data set, about four percent of those had been identified as being potentially bad providers by either property/casualty or healthcare insurers.”

The National Health Care Anti-Fraud Association identified the most common types of health care provider fraud as:

• Billing for services that were never rendered – either by using genuine patient information, sometimes obtained through identity theft, to fabricate entire claims or by padding claims with charges for procedures or services that did not take place.
• Billing for more expensive services or procedures than were actually provided or performed, known as “upcoding”, which often requires the accompanying “inflation” of the patient’s diagnosis code to a more serious condition consistent with the false procedure code.
• Performing medically unnecessary services solely for the purpose of generating insurance payments – seen frequently in nerve-conduction and other diagnostic-testing schemes.
• Misrepresenting non-covered treatments as medically necessary covered treatments for purposes of obtaining insurance payments – widely seen in cosmetic-surgery schemes, in which non-covered cosmetic procedures such as “nose jobs” are billed to patients’ insurers as deviated-septum repairs.
• Falsifying a patient’s diagnosis to justify tests, surgeries or other procedures that aren’t medically necessary.
• Unbundling – billing each step of a procedure as if it were a separate procedure.
• Billing a patient more than the co-pay amount for services that were prepaid or paid in full by the benefit plan under the terms of a managed care contract.
• Accepting kickbacks for patient referrals.

There has been a shift in the way this type of fraud is investigated, says Fannin. Instead of looking at just one bill or one claim, the focus is on the medical provider.

“The focus that we see nowadays is more on the true medical provider,” Fannin says. “Looking at them in an aggregate fashion so that you get a true feel for how that provider’s patterns develop from a diagnostic, treatment and billing perspective.”

Fannin says that carriers looking at just one provider bill may incorrectly assume a billing code error or an incorrectly billed amount when in actuality, the provider is doing the same thing on every bill.

“Whenever you look at say a four year history of this medical provider, you aggregate all that information, it gives you a different perspective,” Fannin says.

Fannin describes the three core types of medical claims fraud typically seen by property/casualty insurers:

Truly overcharging or over treating, sometimes in collusion with patients who want to inflate their claim.

Fannin says some issues with bill level fraud prevention strategies include making sure a carrier is truly aggregating all the medical bills to a particular doctor.

“A doctor may treat patients by operating at multiple facilities. They may be at a clinic on one day, working in a hospital another day. How they appear on the medical bills could appear differently. It could be Dr. J. Smith at one place, Dr. J.T. Smith at another and Dr. Tom Smith at another,” Fannin explains. “You have to have the ability to recognize that all of these individuals are actually one provider before you can actually start aggregating and analyzing their information.”

Fannin offers an example where a customer believed that it had about 400,000 medical providers in their data set, but upon additional analysis it was discovered there were only 250,000 medical providers.

“One provider was actually in their data set 63 different ways,” adds Fannin.

The analysis of that company’s medical provider data also revealed 400 deceased providers.

“Our records showed that the individual originally connected with that Tax Identification Number was no longer around, yet there was active treatment and billing going on underneath their identity,” says Fannin.

Identifying potentially problematic medical providers works best by reviewing records for multiple years and drawing all the data sets together, Fannin explains. Predictive analytics helps to understand provider diagnostic billing and treatment patterns.

The analysis includes peer comparison, he says.

“Being able to look at how their patterns compared to others within their same specialty. Chiropractor to chiropractor, physical therapist to physical therapist. You have to be able to do this analysis,” Fannin says. “That also involves bringing in outside, public record data. Are there any sanctions on their license? Any DEA investigations going on? Bringing all that data now together to really get a picture of that doctor. Then finally, it’s having a tool and/or some ability to then display this information in such a way that an investigator can quickly understand and prioritize the physicians that they need to take a look to build their case.”

The cost savings and efficiency gained from analyzing data this way versus the old school way – gathering provider data by pulling old claims and logging the information on an Excel spreadsheet – can have a huge impact on both an insurer’s ability to mitigate the amount of fraud paid as well as the expense associated with investigating it, Fannin says.

“That can take weeks, months, in some cases even years to compile all the information, to truly have an understanding of what’s going on and then to be able to take action on it,” says Fannin. “Today’s tools, some of the very advanced tools that are available to give the SIU investigator the ability to complete, in a couple of hours or a couple of days, what would have taken them weeks or months to do before.”

The documentation needed to prove a case of fraud varies.

“Certainly you would need to show a clear pattern of willful and wanton activity. Then you would also most probably go out and speak with some of the patients potentially to confirm whether or not treatment actually occurred. Were there any kind of discussions?

Then finally, some additional statements from the provider himself as well as some of the staff to make sure that you’ve got a clear picture of the case because when you take this to any prosecutor, they’re going to ask for all this information,” says the LexisNexis director of claims.

A problem with medical provider fraud in particular, Fannin says, is that there is a lot of opportunity to hide behind numbers and coding. ICD 10 coding changes will add additional complexity to SIU investigations. The reason is that the codes are becoming much more specific.

“I want to say it’s increased the number of potential codes that could be used to something like four or five times,” says Fannin.

Another problem is that sophisticated rings involving organized crime families, both foreign and domestic, are working the medical provider fraud angle.

“A lot of Russian mobs are now getting involved to run these very complex rings. Whenever you think about trying to basically take a case that might involve the local mafia and build that case and take it into court, it’s very difficult,” Fannin says.

While investigating any type of fraud is no easy task, it can lead to some interesting discoveries.

Fannin cites an example where a carrier reviewed medical fraud related data only to discover that a particular auto body shop continuously showed up in the same claims. When the carrier conducted further analysis, the data revealed that claims involving the auto body shop always had that same medical provider associated with it.

The example highlights the value of communication among carriers to reveal these types of connections, Fannin says.

“I touched on the ring fraud earlier. A single carrier might see a few of these connections in their data but it’s not a big enough, bright enough picture for them to understand what’s going on,” Fannin says. “There needs to be more combining of information, more sharing of information and the analysis of that information to truly help the industry fight, especially these complex ring activities.”