How real-time data 'revolutionized' fraud prosecutions [Q&A]

Editor's note: This is the first of a two-part interview with healthcare fraud enforcement expert Gejaa Gobena. In part one, he talks to FierceHealthPayer AntiFraud about improvements in access to data and using analytics to build cases. Part two will focus on the implications of the Yates memo and the impact of large national fraud busts. 

Data analytics has emerged as an indispensable new tool in the federal government’s quest to root out healthcare fraud. Federal officials credited last month’s historic fraud bust that charged more than 300 individuals with fraud schemes totaling $900 million to the increasingly collaborative effort between fraud investigators and data analysts.

While much of the attention surrounding data analytics has been focused on detection and prevention, access to real-time data has emerged as a critical resource for the government’s top attorneys tasked with prosecuting those charged with fraud, according to Gejaa Gobena, a partner at Hogan Lovells in the District of Columbia (pictured above) and the former deputy chief of the fraud section in the Department of Justice’s criminal division.

FierceHealthPayer AntiFraud: You were with the DOJ for more than 13 years prosecuting fraud cases, including three as deputy chief. How has the department's approach to fraud evolved over the years?

Gejaa Gobena: I would say the biggest way the department has evolved in terms of its healthcare investigations and prosecutions, whether it’s civil or criminal, is in its ability to use data to make cases.

When I first started in 2003, if I wanted to get data to test allegations, I would have to reach out to the Medicare contractors who administered the program, and that would sometimes be a lengthy process. They might get back to you weeks, if not months, later. Sometimes the analysis was done well, but you would have more questions in the interim that popped up and you’d have to go back again. That data collection and analysis would take a long time and slow down investigations.

Starting in 2009 when the Obama administration implemented its HEAT [Health Care Fraud Prevention and Enforcement Action Team] task force, one of the main mandates was to make sure data was available in real time. That was a radical change for prosecutors and investigators. You didn’t have that lag with respect to getting data, and you didn’t have a huge lag in terms of being able to analyze the data.

That access to real time data really revolutionized the way the government has been able to investigate and prosecute healthcare fraud cases. And it helps not just in evaluating allegations that came in, but I know when I was in the criminal division, we’d use that information prospectively to see trends, think about where we wanted to focus investigative efforts and also look at specific future targets to go after.

FHPAF: Aside from timeliness, how does that data help you build a case?

Gobena: One of the key things prosecutors look for in the data is how a provider, drug company or device manufacturer compares to others.

A lot of times you can look at that data and see the peer providers, how they operate, how they bill and what they bill for. And you can compare them to the entity or person you’re investigating. Often, in cases of fraud, prosecutors would know there were differences in the way that those entities billed and that they were outliers. Outlier analysis is a cornerstone of the way they look at providers.

One other thing they were able to do over time is to get better at how they actually analyze the data in terms of the kinds of analytical frameworks and models they use to explore allegations against particular providers and look prospectively at what is going on in a particular area, whether that’s home health or hospice or different types of billings.

FHPAF: When you’re bringing criminal charges against a provider, how is that data used in conjunction with other aspects of the case to obtain that guilty verdict?

Gobena: You combine data analytics and information you would get from traditional sources, such as cooperators, witnesses, documentary evidence or bank records, and create a composite image of the fraud scheme.

The fact about data that criminal prosecutors like a lot is the old adage that data doesn’t lie. Defense counsels are usually very good at challenging witnesses, especially cooperators because they are operating under their cooperation agreement hoping for leniency. Data is data. It is what it is, and the extent to which prosecutors are able to argue that there are anomalies that are corroborated by witness testimony as to the nature of those anomalies, data becomes a key part of these prosecutions.

FHPAF: What are the most important fraud trends in healthcare right now and how is data influencing them?

Gobena: One of the interesting trends to be on the lookout for is to what extent medical necessity cases are going to end up prosecuted as criminal cases. Medical necessity cases pop up in a variety of different industries as services billed by a hospital, hospice, or home health or services billed by individual providers.

Traditionally, that’s an area that wasn’t really the focus of a ton of criminal activity. I think with the data analytics capabilities, we could see that area subject to potential criminal prosecutions because as I pointed out earlier, there are things you can see in the data now that you can use to corroborate any information you have.

The challenge with medical necessity cases is it’s often a doctor’s decision or a medical professional’s decision that is being challenged, but I think prosecutors look at whether they can use data to challenge some of those decisions when there is other evidence indicating a lack of medical necessity.

Editor's note: This interview has been edited for clarity and length. 

Suggested Articles

Civica Rx, the non-profit drug company formed by a collection of hospitals to help control generic drug supplies and prices, is putting down roots.

ONC is moving another step closer to implementing a framework designed to improve data sharing between health information networks.

Welcome news to many health IT stakeholders: HHS announced Friday that it is extending the comment period for two proposed interoperability rules.