Scrutinize sampling to challenge overpayment demands
Scrutinize sampling to challenge overpayment demands
Key areas to challenge include sample size, randomness, stratification, and relative error
Even as overpayment assessments continue to surge, many health care providers have yet to learn how to respond effectively to the Health Care Financing Administration’s (HCFA) aggressive audit and post-payment reviews of provider claims, according to several health care attorneys.
"We are definitely seeing more effort by HCFA to influence administrative law judges (ALJs) with regard to this issue, and many of those efforts are probably improper," says Lester Perling, a health care attorney with Broad & Cassel in Fort Lauderdale. He says that is because recent efforts to clarify sampling requirements actually have made matters worse.
Worse yet, "a lot of providers tend to roll over and pay it and don’t do anything about it," says health care attorney Kathy Fritz, of Davis Wright in Seattle.
That approach can carry a heavy price tag, warns Fritz. Some hospitals have even suspended practitioners simply because they were under investigation, even though they weren’t even close to being excluded from the program, she notes.
Fritz says the good news is that HCFA’s overpayment demands often can be successfully challenged, and the place to start is by demanding the government’s documentation. In fact, Fritz says the adequacy of the sample often becomes a moot point because the government fails the documentation test.
"A major point that seems to have escaped most carriers is that they have an absolute obligation under law to provide the provider with all of their information regarding the extrapolation and stratification of sample size and how they conducted their audit," she asserts.
"I have yet to get that information," she reports. "When that happens at a carrier hearing, I win slam-dunk because their extrapolation gets thrown out."
Beyond that, Perling says failure by the government or a contractor to perform the sample study correctly is one of the most effective ways to challenge an overpayment determination. "This can have a very significant impact on the amount due, often reducing the overpayment by a very significant percentage," he says.
Nobody argues the need for scientific statistical sampling and extrapolation in cases of overpayment determination. "It would be impossible or prohibitively expensive to audit all claims in the overall population using a census covering 100% of these claims," explains Michael Intriligator, statistics professor at the University of California at Los Angeles.
On the other hand, the government often falls short in making its case, says Intriligator, who has testified as an expert witness in numerous appeals before ALJs and the Departmental Appeals Board. In fact, he says there are several areas where carriers and intermediaries often go wrong when it comes to sampling and extrapolation.
Here are four key areas to challenge carriers and intermediaries:
s Sample size. Frequently, Intriligator says the sample size of the claims selected is too small and inconsistent not only with generally accepted statistical principles, but with HCFA’s own guidelines for a "basic sample size." In some instances, the number of claims actually selected can be less than a tenth of the number required, he says.
s Randomness of the sample. To be statistically valid, the sample must be selected at random, with no biases or other distortions that could make it not "representative," according to Intriligator. Sampling that specifically omits low-charge claims, for example, would not yield a reasonable sample, he says.
He points out that the HHS Office of the Inspector General’s software package RAT-STATS has an associated manual that describes how to use it, but no documentation to confirm that it is providing valid results other than its random number generator. As a result, Intriligator says, except for the random number generator, none of its components have been audited and verified independently to determine whether they perform properly.
s Stratification of the sample. A third area of possible error is improper stratification. That process divides the population into different subpopulations that are relatively homogeneous, even when the overall population is heterogeneous. But Intriligator says that must be based on some "real differences" that caused the need for stratification in the first place.
For example, hospital services might be stratified into inpatient/outpatient or other categories, while physician services might be stratified by diagnostic categories, he explains.
s Relative error. A fourth area Intriligator points to is estimated relative error, which he says is the single-best measure of the variability in the estimate. "If it exceeds the HCFA tolerance levels, then the whole study is questionable as exhibiting too much variability," he explains. "We have had some cases dismissed simply on the basis of an unacceptably high estimated relative error."
"Any one of these issue areas or some combination of them could represent a basis for challenging the sampling/extrapolation," he says. It is also possible to challenge the qualifications and capabilities of those performing the study.
Perling and Intriligator currently are working on an upcoming report on this subject for the American Health Lawyers Association.
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