Federal appeals court won't judge statistical sampling use in SNF case

The case takes on the use of the controversial statistical sampling method
The case takes on the use of the controversial statistical sampling method

An appeals court this week declined to rule on the use of statistical sampling in False Claims Act cases, leading debate over the controversial fraud investigation method to continue.

The case in question, U.S. ex rel. Michaels v. Agape Senior Community, involves a skilled nursing chain accused by two former employees of submitting false hospice claims to Medicare. The provider — backed up by industry heavyweights including the American Health Care Association — opposed the whistleblowers' reliance on statistical sampling to prove their case.

Statistical sampling uses a “representative sample” of submitted claims believed to be inflated, and compares them to the total number of claims submitted by the provider during the time when violations were said to occur. Federal and district courts have been split over the method, which has been called “trial by formula,” and a “giant sledgehammer … forcing defendants into pretrial settlements.”

The U.S. Court of Appeals for the Fourth Circuit on Tuesday agreed with a lower court's ruling that the federal government has absolute power to veto proposed settlements in FCA cases, but it declined to issue guidance on the use of statistical sampling.

The court's dismissal of the statistical sampling issue follows its earlier concerns of whether the appeal of a previous court ruling, which in this instance denied the whistleblowers use of statistical sampling to back up their claims, was valid.

“[A]lthough we understand and appreciate the District Court's desire to obtain review of its statistical sampling ruling prior to undertaking complex trial proceedings, we are constrained to dismiss that aspect of the [whistleblowers'] appeal as improvidently granted,” wrote Judge Robert B. King in the opinion.

The case will return to trial court for further argument over the use of statistical sampling.