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San Bois Health Services, Inc. v. Hargan

United States District Court, E.D. Oklahoma

November 6, 2017

SAN BOIS HEALTH SERVICES, INC., Plaintiff,
v.
ERIC D. HARGAN, Acting Secretary and Deputy Secretary of the United States Department of Health and Human Services, [1]Defendants.

          ORDER [2]

          HONORABLE RONALD A. WHITE UNITED STATES DISTRICT JUDGE

         San Bois Health Care Services, Inc. (hereinafter “Plaintiff”) brought this Complaint for Judicial Review pursuant to 42 U.S.C. § 1395ff(b), seeking review of the final decision of the Secretary of the United States Department of Health and Human Services (hereinafter “Defendant”) finding that the Medicare program overpaid Plaintiff for certain claims and that the extrapolated overpayment was based upon a valid sampling methodology. Now before the court is Plaintiff's motion to reverse the decision of the Medicare Appeals Council (hereinafter “the Council”) [Docket No. 24]. The court has carefully considered the administrative record and the briefing filed in this case, as well as the oral argument heard on September 27, 2017. The court herein finds that the Council applied the proper legal standards and the Council's decision is supported by substantial evidence in the administrative record. Accordingly, Plaintiff's motion is denied, and this action is dismissed.

         BACKGROUND

         Medicare

         Medicare is a federal health insurance program for eligible elderly and disabled persons that is administered by CMS. 42 U.S.C. § 1395, et seq. CMS contracts with private entities to perform administrative functions on its behalf. Medicare claims are processed by MACs. MACs process and pay hundreds of millions of claims per year; thus it is difficult for CMS to detect and recover improper Medicare payments. CMS, therefore, empowered its contractors to initially authorize payment for certain claims and then later use statistical sampling in post-payment audits to estimate overpayments.

         Medical review audits are undertaken by several types of contractors, including ZPICs. Audits are governed by Health Care Financing Administration Ruling No. 86-1[3] and the MPIM.[4]Health Integrity, LLC is the ZPIC responsible for performing these audits in Oklahoma. Q² Administrators, LLC is the QIC that reviews appeals of claim denials. MAXIMUS Federal Service is the administrative qualified QIC that takes over after an ALJ issues a decision.

         Statistical Sampling, Ruling 86-1 and the MPIM

         Under Ruling 86-1, statistical sampling “creates a presumption of validity as to the amount of an overpayment which may be used as the basis for recoupment.” Ruling 86-1, at 11. If a provider disagrees with the outcome, the provider may “attack the statistical validity of the sample, or it could challenge the correctness of the determination in specific cases identified by the sample.” Id. The provider, however, has the burden to overcome the presumption of validity of the sampling and extrapolation methodology. Id.; Maxmed Healthcare, Inc. v. Price, 860 F.3d 335, 339 (5th Cir. 2017).

         Chapter 8 of the MPIM sets out the guidelines for ZPICs “on the use of statistical sampling in their reviews to calculate and project (i.e. extrapolate) overpayment amounts to be recovered by recoupment, offset or otherwise.” MPIM, Ch. 8, § 8.4.1.1. The guidelines “are provided to ensure that a statistically valid sample is drawn and that statistically valid methods are used to project an overpayment, ” but the failure of a ZPIC to follow one or more of the requirements “does not necessarily affect the validity of the statistical sampling that was conducted or the projection of the overpayment.” Id. “An appeal challenging the validity of the sampling methodology must be predicated on the actual statistical validity of the sample as drawn and conducted.” Id.

         The major steps involved in conducting statistical sampling are:

(1) selecting the provider or supplier;
(2) selecting the period to be reviewed;
(3) defining the universe, the sampling unit, and the sampling time frame;
(4) designing the sampling plan and selecting the sample;
(5) reviewing each of the sampling units and determining if there was an overpayment or an underpayment; and as applicable;
(6) estimating the overpayment.

MPIM, Ch. 8, § 8.4.1.3. The “universe” consists of “all fully and partially paid claims submitted by the supplier for the period selected for review and for the sampling units to be reviewed.” MPIM, Ch. 8, § 8.4.3.2.1 B. The “sampling unit” is defined by the sample design chosen by the contractor. The most common sample designs are simple random sampling, systematic sampling, stratified sampling, and cluster sampling, or a combination of these. MPIM, Ch. 8, § 8.4.4.1.

         Regardless of the method of sample selection used, the ZPIC must follow a procedure that results in a probability sample. MPIM, Ch.8, § 8.4.2 (emphasis added). For a procedure to be classified as a probability sampling, the following two features must apply:

(1) It must be possible, in principle, to enumerate a set of distinct samples that the procedure is capable of selecting if applied to the target universe. Although only one sample will be selected, each distinct sample of the set has a known probability of selection. It is not necessary to actually carry out the enumeration or calculate the probabilities, especially if the number of possible distinct samples is large - possibly billions. It is merely meant that one could, in theory, write down the samples, the sampling units contained therein, and the probabilities if one had unlimited time; and
(2) Each sampling unit in each distinct possible sample must have a known probability of selection. For statistical sampling for overpayment estimation, one of the possible samples is selected by a random process according to which each sampling unit in the target population receives its appropriate chance of selection. The selection probabilities do not have to be equal but they should all be greater than zero. In fact, some designs bring gains in efficiency by not assigning equal probabilities to all of the distinct sampling units.

Id. If a particular probability sample design is properly executed, “then assertions that the sample and its resulting estimates are ‘not statistically valid' cannot legitimately be made. In other words, a probability sample and its results are always ‘valid.'” Id.

         Simple random sampling

…involves using a random selection method to draw a fixed number of sampling units from the frame without replacement, i.e., not allowing the same sampling unit to be selected more than once. The random selection method must ensure that, given the desired sample size, each distinguishable set of sampling units has the same probability of selection as any other set - thus the method is a case of “equal probability sampling.” An example of simple random sampling is that of shuffling a deck of playing cards and dealing out a certain number of cards (although for such a design to qualify as probability sampling a randomization method that is more precise than hand shuffling and dealing would be required.)

MPIM, Ch. 8, § 8.4.4.1.1.

         The MPIM explains that stratified sampling,

…involves classifying the sampling units in the frame into non-overlapping groups, or strata. The stratification scheme should try to ensure that a sampling unit from a particular stratum is more likely to be similar in overpayment amount to others in its stratum than to sampling units in other strata. Although the amount of an overpayment cannot be known prior to review, it may be possible to stratify on an observable variable that is correlated with the overpayment amount of the sampling unit. Given a sample in which the total frame is covered by non-overlapping strata, if independent probability samples are selected from each of the strata, the design is called stratified sampling. The independent random samples from the strata need not have the same selection rates. A common situation is one in which the overpayment amount in a frame of claims is thought to be significantly correlated with the amount of the original payment to the provider or supplier. The frame may then be stratified into a number of distinct groups by the level of the original payment and separate simple random samples are drawn from each stratum. Separate estimates of overpayment are made for each stratum and the results combined to yield an overall projected overpayment.
The main object of stratification is to define the strata in a way that will reduce the margin of error in the estimate below that which would be attained by other sampling methods, as well as to obtain an unbiased estimate or an estimate with an acceptable bias. The standard literature, including that referenced in Section 3.10.10, contains a number of different plans; the suitability of a particular method of stratification depends on the particular problem being reviewed, and the resources allotted to reviewing the problem.

MPIM, Ch. 8, § 8.4.4.1.3. The MPIM further provides with regard to stratified sampling:

Generally, one defines strata to make them as internally homogeneous as possible with respect to overpayment amounts, which is equivalent to making the mean overpayments for different strata as different as possible. Typically, a proportionately stratified design with a given total sample size will yield an estimate that is more precise than a simple random sample of the same size without stratifying. The one highly unusual exception is one where the variability from stratum mean to stratum mean is small relative to the average variability within each stratum. In this case, the precision would likely be reduced, but the result would be valid. It is extremely unlikely, however, that such a situation would ever occur in practice. Stratifying on a variable that is a reasonable surrogate for an overpayment can do no harm, and may greatly improve the precision of the estimated overpayment over simple random sampling. While it is a good idea to stratify whenever there is a reasonable basis for grouping the sampling units, failure to stratify does not invalidate the sample, nor does it bias the results.
If it is believed that the amount of overpayment is correlated with the amount of the original payment and the universe distribution of paid amounts is skewed to the right, i.e., with a set of extremely high values, it may be advantageous to define a “certainty stratum”, selecting all of the sampling units starting with the largest value and working backward to the left of the distribution. When a stratum is sampled with certainty, i.e., auditing all of the sample units contained therein, the contribution of that stratum to the overall sampling error is zero. In that manner, extremely large overpayments in the sample are prevented from causing poor precision in estimation. In practice, the decision of whether or not to sample the right tail with certainty depends on fairly accurate prior knowledge of the distribution of overpayments, and also on the ability to totally audit one stratum while having sufficient resources left over to sample from each of the remaining strata.
Stratification works best if one has sufficient information on particular subgroups in the population to form reasonable strata. In addition to improving precision there are a number of reasons to stratify, e.g., ensuring that particular types of claims, line items or coding types are sampled, gaining information about overpayments for a particular type of service as well as an overall estimate, and assuring that certain rarely occurring types of services are represented. Not all stratifications will improve precision, but such stratifications may be advantageous and are valid.
Given the definition of a set of strata, the designer of the sample must decide how to allocate a sample of a certain total size to the individual strata. In other words, how much of the sample should be selected from Stratum 1, how much from Stratum 2, etc.? As shown in the standard textbooks, there is a method of “optimal allocation, ” i.e., one designed to maximize the precision of the estimated potential overpayment, assuming that one has a good idea of the values of the variances within each of the strata. Absent that kind of prior knowledge, however, a safe approach is to allocate proportionately. That is, the total sample is divided up into individual stratum samples so that, as nearly as possible, the stratum sample sizes are in a fixed proportion to the sizes of the individual stratum frames. It is emphasized, however, that even if the allocation is not optimal, using stratification with simple random sampling within each stratum does not introduce bias, and in almost all circumstances proportionate allocation will reduce the sampling error over that for an unstratified simple random sample.

MPIM, Ch. 8, § 8.4.11.1.

         While probability sampling pursuant to the MPIM results in a point estimate - the difference between what Medicare paid and what Medicare should have paid - in most situations, the lower limit of a one-sided 90 percent confidence interval is used as the amount of overpayment demanded for recovery from the provider. Id. at 8.4.5.1.

         Medicare Administrative Appeals Process

         Once a contractor determines that there was an overpayment, a provider may appeal. The ...


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