That system was child welfare.
Grabbing hold to my beliefs that laws, any type of laws (i.e. theoretical, statutory) were made to be challenged, either through alternative models, robust testing, or the democratic process, I went about my merry way to prove that the level of fraud in child welfare was statistically significant through sampling.
Using extapolated data of Wayne County, Michigan expenditures, not only did I find a positive, direct correlation in the rate of poverty with the rate of child welfare cases, but I also found unique cost-reimbursement patterns paralleling child welfare campaign months.
Stochastic analyses took the statistical significance to entirely different levels of reliability.
Parsimony led me to Medicaid fraud in child welfare because its homogeneity reeked the loudest.
If the federal administrations do not have the resources to go after Medicaid fraud in child welfare, then the courts should listen to data from an original source.
The technology of statistics should be adopted as an evidentary standard in a court of law. It is time to get rid of the horse and buggy.
Stay tuned for further iterations, ha ha.
Fourth Circuit to Rule on Use of Statistical Sampling to Prove FCA Liability
The U.S. Court of Appeals for the Fourth Circuit has agreed to hear an interlocutory appeal on the use of statistical sampling as a means of proving liability under the False Claims Act (FCA). While statistical methods of proof have been used with respect to damages, relatively few courts have considered whether such methods are ever appropriate to establish liability under the FCA. Thus, the court’s ruling has the potential to shape practice in this area moving forward.
The case, United States ex rel. Michaels v. Agape, concerns allegations that a network of 24 nursing homes throughout South Carolina submitted fraudulent claims to Medicare, Medicaid and Tricare for care that was not medically necessary. Due to the large volume of potentially fraudulent claims—over 50,000 claims were submitted during the relevant time period—relators sought to use statistical sampling to prove that defendants had submitted false claims. Specifically, the relators sought to have their experts review a small percentage of the claims, determine what percentage of those claims were fraudulent and extrapolate over the entire universe of submitted claims. The district court rejected the approach but certified the question for interlocutory appeal.
The district court was correct to be skeptical of statistical sampling to prove liability, and the relators will have an uphill battle to convince the appeals court that their proposed method of proof is sound.
First, as the district court noted, this is not a case where the relevant evidence is unavailable and statistical sampling presents the only possible method of proof. In fact, all the documentation concerning the allegedly fraudulent claims exists and is fully accessible. The relators’ argument is simply that it would take too much time to review such a large volume of data. As the district court stated, such shortcuts are inappropriate in a case where the alleged fraud is that services provided were not medically necessary—a “highly fact intensive inquiry involving medical testimony after a thorough review of the detailed medical chard of each individual patient.”
Second, the use of statistical sampling is not guaranteed to shorten the trial because the defendants still retain the right to present evidence on each individual claim. To force the defense to also rely on a sample of the claims and prevent the presentation of evidence on the remainder of the individual claims would, as the defendants argued, deny them of their constitutional right to a jury trial on the facts.
The Fourth Circuit’s ruling on this issue has the potential to either settle the law in this area or open the door to speculative methods of proof in a number of areas of FCA litigation.
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