A wave of letters containing data about billing aberrations is being sent. Some come from MACs and offer details about how your coding distributions vary from state and/or national norms, but a variety of additional contractors are sending letters as well.
For example, one company I had never heard of previously, “A+ Government Solutions,” recently indicated that it has a government contract to disseminate comprehensive billing reports.
These reports, or CBRs, state that they are educational, and often add that they can assist in the self-audit process. The letters from the MACs often request that you conduct a self-audit and make a refund if appropriate.
If you receive such a letter, what should you do? View the issue raised in the letter as you would any other statistical outlier; investigate, but don’t jump to conclusions. It is entirely normal to have statistical outliers. In fact, the absence of any statistical outliers, ironically, would be a statistical outlier. Don’t be afraid of being an anomaly; be afraid of being an anomaly without a good reason for it. The key is determining why the outlier exists.
Making the Case for an Open Mind
When examining outliers, perhaps the most important skill to possess is maintaining an open mind. Consider every possibility. A physician with very high RVUs may be a hard worker, may be lying about services rendered, may not understand coding rules, or may have experienced some sort of data error, just to name a few possibilities. If there is a data error, it may be the fault of the physician or it may be due to some difficult-to-determine sequence of errors. Early in my career I was in a contentious fight with an insurer about a radiology group’s “high pricing.” The insurer claimed that the group’s charges ranked in the top 15 percent of all providers in the country (being in the top 15 percent isn’t necessarily a problem – after all, there has to be a top 15 percent among every group – but this was inconsistent with this group’s other fees). It was while discussing some particular codes that we discovered that the insurer did not understand the use of modifiers. The radiology group’s global bill for technical and professional charges was being compared with other organizations’ technical-only bills. The insurer’s data was totally flawed, and it was merely chance that allowed us to determine that. I have to admit that when I was considering the situation, the possibility that the insurer didn’t understand what “TC” and “-26” meant did not occur to me. A good auditor has to entertain every possibility.
Challenging Other’s Data
It is particularly challenging to analyze the validity of someone else’s data. You can verify that the demographic data the reviewer has is correct (do they have the physicians classified under the right specialty, for example?) But you can’t evaluate some factors that might make you “unique.” If you are the only geriatric psychiatrist in a state, for example, it is entirely reasonable for the number of Medicare visits you have to be far above the norm. But how do you know what mix of psychiatrists is in a sample? A cardiac hospital will have a very different patient mix than a suburban hospital, and it is very difficult to determine whether any given facility’s patients are atypical (sicker, older, etc.). Drawing conclusions from this data is perilous. It can be easy to suffer from Garrison Keillor’s so-called Lake Wobegon effect and think that everyone you associate with is “above average.” This creates the risk of dismissing data anomalies before you investigate them thoroughly. It can be equally improper, however, to assume that the presence of data irregularities means you are doing something incorrectly. Because perfect data analysis is rarely feasible, you may be in a position of using a best guess to determine whether data aberrancy means you should change your behavior, your coding, or nothing at all. The best approach generally is to take a few services of the type in question and carefully review them. Consider the coding, the documentation, the medical necessity and other details to confirm that the services were appropriate.
It is important to understand that while a letter may request that you perform a self-audit, you are not obligated to do so. The only legal obligation imposed by Medicare is to report and return overpayments. A data irregularity does not demonstrate that there has been an overpayment, so no refund is required based on that alone. Since a refund can be viewed as an admission, I generally discourage clients from refunding money unnecessarily.
Generally speaking, it is best to limit refunds to situations in which you have concluded that your billing was incorrect. Also, while you are not required to perform a self-audit, I recommend that at a minimum you perform enough of a self-examination to verify that in the event of an outside audit, you are comfortable you could defend the claims. View the letter as a shot across the bow, warning of a possible outside audit. As long as you conclude that you are comfortable the claims are defensible, no additional action will be necessary.
About the Author
David Glaser is a shareholder in Fredrikson & Byron’s Health Law Group and helped establish its Health Care Fraud & Compliance Group. David helps healthcare entities negotiate the maze of healthcare regulations, providing advice about risk management, reimbursement and business planning issues. He has considerable experience in healthcare regulation and litigation, including compliance, criminal and civil fraud investigations, and reimbursement disputes.
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