Qui Notes
June 8, 2020

Fifth Circuit Holds Statistics Don't (Show a) Lie

Qui Notes: Unlocking the False Claims Act

Recent years have brought an increase in qui tam suits filed by "professional" relator firms established specifically for the purpose of bringing False Claims Act cases. Although DOJ has used its (c)(2)(A) authority to root out some of these cases, particularly where information was gathered under dubious circumstances, many proceed to litigation based largely on the relator's analysis of publicly available data. In one of the more closely watched cases, United States ex rel. Integra Med Analytics, LLC v. Baylor Scott & White Health, the relator purported to apply proprietary "algorithms and statistical processes" to publicly available CMS data to find fraud by a prominent Texas hospital system. Last week, the Fifth Circuit affirmed dismissal of that case because the data from which the relator inferred fraud was equally consistent with "a legal and obvious alternative explanation." No. 19-50818, 2020 WL 2787652 (5th Cir. May 28, 2020).

Relator's claims concerned Baylor Scott & White Health's use of secondary diagnosis codes to increase Medicare reimbursement. Secondary diagnosis codes are used to reflect conditions that although not the principal basis for hospitalization, either coexist at the time of admission, develop subsequently, or affect the treatment and/or length of stay. When added to a reimbursement claim, they result in the claim being considered a "complication or comorbidity" (CC) or a "major complication or comorbidity" (MCC), which are reimbursed at higher levels. The relator alleged that Baylor engaged in a variety of schemes to improperly "upcode" secondary diagnosis codes. The crux of the relator's case was that publicly available inpatient claims data showed that Baylor coded certain high-value MCCs at a rate significantly above the national average. According to the relator, fraud was the only explanation for the difference. The relator also relied on a handful of statements from a former Baylor employee and statements on the social media page of a hospital executive. Baylor moved to dismiss under Rules 12(b)(6) and 9(b) and the FCA's public disclosure bar. The district court dismissed for failure to plead falsity with plausibility and particularity, declining to reach the public disclosure bar question.

The Fifth Circuit affirmed, reasoning that a relator cannot rely on statistical analysis to plead falsity where, although fraud could be inferred from the data, "it is also consistent with a legal and obvious alternative explanation." Here, the court found that the relator's statistical analysis was "consistent with both Baylor having submitted fraudulent Medicare reimbursement claims to the government and with Baylor being ahead of most healthcare providers in following new guidelines from CMS." Noting that CMS recently increased the number of secondary diagnoses eligible for additional reimbursement, in the same data where the relator found alleged fraud, the Fifth Circuit found evidence that Baylor was simply ahead of the curve in implementing the new CMS guidelines. The court noted that although Baylor's use of certain codes was higher than other hospitals, the data showed coding rates of various hospitals starting to converge. The court held the relator's remaining allegations based on a former Baylor employee's statements insufficient under Rule 9(b).

Although the Fifth Circuit noted that it was not foreclosing the possibility that statistical data could be used to meet pleading requirements for an FCA claim, its decision underscores the limitations of such data. And it provides an important tool for companies increasingly faced with the prospect of defending FCA suits based on professional relators' parsing of public data.

© Arnold & Porter Kaye Scholer LLP 2020 All Rights Reserved. This blog post is intended to be a general summary of the law and does not constitute legal advice. You should consult with counsel to determine applicable legal requirements in a specific fact situation.

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