Statistical sampling is an important tool for litigants asserting complex claims in cases involving a large universe of related factual issues. Statistical sampling has become more commonplace over the years, and it is now frequently used in antitrust, employment discrimination, toxic torts, and voting rights cases. In False Claims Act (FCA) cases, statistical sampling has been used to determine damages, and it has proven useful in cases involving a large number of claims, rendering claim-by-claim review extremely burdensome. For example, in Medicare or Medicaid fraud cases in which fraud is alleged in connection with the submission of claims for payment arising out of the treatment of thousands of patients, courts have allowed audits of a valid statistical sample of patient files to be used as evidence of overall damages. Now, a court has only recently ruled that statistical sampling can be utilized to help determine liability in FCA cases.
In United States ex rel. Martin v. Life Care Centers of America, Inc., No. 1:08-cv-00251-HSM-WBC (E.D. Tenn. Sept. 29, 2014), the parties presented Judge Harry Mattice of the United States District Court for the Eastern District of Tennsesee with the question of whether statistical sampling was a viable method to prove liability in FCA cases. The defendant, Life Care Centers of America (“Life Care”), is an owner of skilled nursing facilities nationwide. The government and qui tam relator alleged that Life Care pressured its personnel to target the patients with the highest Resource Utilization Group (“RUG”) levels and longer-than-average lengths of stay and to provide medically unnecessary services in order to maximize Medicare revenue.
Rather than embark on the impractical task of reviewing every claim filed within a nearly seven-year period, the government sought to use a random sample of 400 patient admissions from 82 Life Care facilities to make determinations about a total universe of 54,396 total patient admissions and 154,621 total claims. Life Care moved for summary judgment on the grounds that statistical sampling was not a viable method of proving FCA claims. The court denied Life Care’s motion.
The court examined how statistical sampling could be utilized to prove the required elements of a FCA claim. It first found that statistical sampling could be used as proof that a specific false claim was made. The court reasoned that “the purpose of statistical sampling is precisely for these types of instances in which the number of claims makes it impracticable to identify and review each claim and statement,” and that evidence that claims within the sample were false could be extrapolated to the universe of claims.
The court next found that sampling could be utilized to help prove the falsity of claims. In spite of the fact that individualized factors could affect each claim, the court found that “the fact that these factors exist and are likely unique to each patient does not necessarily preclude the use of statistical sampling.” Similarly, the court approved the use of proof of Life Care’s knowledge of the falsity of the statements within the sample as evidence of overall knowledge. Finally, the court held that the materiality of the false statements could also be proven by statistical sampling, meaning that examples from the sample where it is proven that the false statement had the tendency to influence payment of money in response to the false claim can be extrapolated to help prove materiality of the statements in the universe of claims. In a separate opinion, the court rejected a challenge to the methodology employed by the government’s statistical expert, and the court found that statistical experts need not be experts in the substantive field (healthcare, in this case) to which their statistical analysis is being applied.
In its decisions, the court itself recognized how important a tool statistical sampling was in rooting out fraud. It stated that “[t]he purpose of the FCA as well as the development and expansion of government programs as to which it may be employed support the use of statistical sampling in complex FCA actions where a claim-by-claim review is impracticable,” and that “[g]iven the large number of claims that can be submitted by a single entity to be reimbursed by Medicare, it is often not practicable to do a claim-by-claim review of each allegedly false claim in a complex FCA action.”
The court’s ruling in Martin provides litigants with a significant tool in FCA cases that involve large universes of potentially false claims. The court also recognized that accepting the defendant’s categorical argument against statistical sampling would unacceptably weaken the FCA: “If the Court were to reach the conclusion urged by the Defendant–that a claim-by-claim review is required in every FCA action and that statistical sampling is never permissible–potential perpetrators of fraud would be emboldened by the fact that a claim-by-claim review is often impractical. Armed with the knowledge that the government could not possibly pursue each individual false claim, large-scale perpetrators of fraud would reap the benefits of such a system. Put another way limiting FCA enforcement to an individual claim-by-claim review would open the door to more fraudulent activity because the deterrent effect of the threat of prosecution would be circumscribed. The Court is unable to conclude that such a result is consistent with the purpose and history of the FCA.”
Thanks to the decision in Martin, qui tam whistleblowers with knowledge of large-scale fraud–be it in the Medicare realm or some other area–will be able to rely on statistical sampling as proof in FCA cases where claim-by-claim reviews would be cumbersome of impossible. This ruling should encourage whistleblowers to come forward when they know of far-reaching frauds against the government, but can only provide details of relatively few specific false claims. Martin is an excellent result for whistleblowers, the government, and citizens who do not want their tax dollars to support large-scale fraudulent endeavors.