September 18, 2014

The Harder You Work, The More Dangerous it Becomes

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Many years ago I attended a compliance conference, and one of the speakers was a former FBI agent who did work with the U.S. Department of Health and Human Services (HHS) and the Office of Inspector General (OIG) on healthcare fraud issues. Back then there were a lot of fire-and-brimstone presentations about gun-wielding, door-breaking raids on medical practices, hospitals, and other healthcare institutions.

From what I could tell, at least in my naivety back then, this constituted an overabundance of bravado that was designed to instill fear into the hearts of healthcare professionals. Now, I’m not saying that there isn’t a reason to be afraid, because I really do believe that the government is out to get us. From what I have read, healthcare fraud occurs infrequently, but when it does, it makes a big impact.

We have all read about the psychiatrist who got nailed for billing for 48 hours of care per day, or the docs who bilked Medicare for millions for billing for services that weren’t provided – or even the famous case with a Houston-area doctor who got caught up in a Nigerian fraud ring. In this latter case, Medicare was billed more than $11 million in less than a year with her name listed as the provider. The point is that fraud happens, and when it does, we usually expect that, for an indictment, the government will have overwhelming evidence to support its case. That, however, is not always the case, and I have worked as an expert on three criminal fraud cases in which the provider was eventually acquitted because the evidence was weak (to say the least). Here’s the scary part: in all three cases, the indictments were based on what the government considered over-utilization, and the data that supported those claims. That’s it. 

I recently testified in a criminal fraud case involving a physician who was indicted for just that reason; the government believed that it was physically impossible for the physician to have provided the volume of services reported during the time period in question. I was engaged as the statistical expert, and my job was to dispute the data that the government submitted as evidence. And not to keep you in suspense, the physician was acquitted of the charges. I didn’t know until later, but if convicted, he would have been facing up to 14 years in prison. While this case is now part of public record, out of deference to the physician, I will refer to him here only as Dr. D.K. He was represented by the legal firm of Kern, Augustine, Conroy & Schoppmann, P.C. out of New Jersey. 

As an introduction, Dr. D.K. is an internal medicine physician whose practice is in the Northeast. He was investigated and subsequently charged with criminal fraud based solely on the volume of services reported to Medicare (and, subsequently, private payors). In his first trial, in which I was not involved, the government presented its data showing that, according to CPT® standard times, work RVUs, and subsequent benchmark data, he was working in excess of 24 hours per day. The government did not, however, produce any real evidence of fraud (such as testimony from patients indicating that the doctor didn’t see them, yet billed for services). This trial ended in a hung jury, and the government then decided to try him again.

For this second trial, I was engaged to review and refute the data presented by the government as evidence of fraud. And this should really raise the hairs on the back of our collective neck, because the government, in this as well as other cases, relied upon data that simply has no scientific or reasonable foundation. The CPT estimated times, for example, all ended with a 0 or a 5. That ought to tell us something about the accuracy of those estimates since there are eight other digits between zero and nine! And what about ranges and error? Does a standard time of 25 minutes mean that it could have ranged from two minutes to 55 minutes depending on the acuity of the patient or the experience of the physician? Some of you probably have heard me say in the past that a point estimate without error and variance is worthless. Even in the RUC study we only got point estimates, so nobody had any idea about how accurate their time estimates were, really. In fact, over the past few years, there has been a great deal of criticism and controversy surrounding the RUC estimates, with many studies indicating that the times were substantially overstated.

These time estimates are what the Centers for Medicare & Medicaid Services (CMS) use to assign work RVUs, and they accept, from what I read, over 90 percent of the RUC recommendations. That would mean that, to a lesser or greater degree, work RVUs are also overestimated. Now I don’t want to critique the use of RVUs, because I use them all the time. RBRVS is a great system for internal use, but because this is a relational model and because of the huge differences in practice dynamics, it is not always a good idea to use external data to benchmark internal data. And this is one of those cases.

Dr. D.K., for the 10-year period in question, saw a limited type of patients requiring limited types of services. In fact, it was his E/M services that really got the ball rolling. But consider this: only a handful of ICD-9 codes accounted for nearly 80 percent of all of the ICD-9 codes he reported. And only a handful of CPT codes accounted for 80 percent of the CPT codes he reported.

In essence, here is an internal medicine physician who for the past 30 years has been doing the same things for the same types of patients over and over again until he became so efficient at his work that if there were a range of values for CPT or RUC time, he would appear at the very bottom. And if that was the case, the number of hours he was estimated to have worked (based on the number of work RVUs reported) would make perfect sense. Add to this the fact that he really did work, on average, 340 days a year and 18 hours a day (substantiated by testimony from hospital staff), and it was not surprising nor unrealistic to see his utilization data at the top of the list. In the end we got Dr. D.K. acquitted, and I can’t even imagine how damaging this whole thing was to him. And even though the government was not able to prove its case, it is not obligated to repair any of the damage it brought upon Dr. D.K. and his family.

Here, because of his experience, lifestyle and sacrifice, Dr. D.K. probably had to spend a fortune to defend himself against an overzealous prosecutor who, if he would have taken the time to really drill down into the data he used to indict the physician, would have realized that the case was not worth pursuing. The scary part is that Dr. D.K. is far from alone. This process of going after providers based solely on their utilization patterns is growing exponentially, as evidenced by all of the articles that followed the release by CMS of the 2012 Medicare Public Use Files on April 9 of this year. I am all for combating fraud and abuse. I don’t want to pay, through my tax dollars, for the costs that Medicare and Medicaid fraud bring to the system. I just don’t think that it should be done in such a reckless way as to bring harm to those who are just doing their jobs.

So, what’s a doctor to do? Well, being prepared is about the best thing of which I can think. Look at your utilization and see what you would think if you were an outsider looking in. Check yourself against your peers to see whether you look like an outlier or whether you might be raising a red flag.

This won’t stop an investigation or an audit, but it will at least put you in a position to be proactive so that if they come in guns blazing, you will be ready to present your case.

About the Author

Frank Cohen is the director of analytics and business intelligence for DoctorsManagement. He is a healthcare consultant who specializes in data mining, applied statistics, practice analytics, decision support, and process improvement. Mr. Cohen is also a member of the National Society of Certified Healthcare Business Consultants (NSCHBC).

Contact the Author

frank@frankcohengroup.com

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