In the realm of False Claims Act (“FCA”) litigation, the emergence of artificial intelligence (“AI”) and data mining technologies has introduced both opportunities and complexities for defense strategies. Historically, qui tam cases have relied heavily on meticulous investigation and insider knowledge to substantiate allegations of fraud. However, the integration of AI-driven data analytics is now providing unprecedented capabilities for potential whistleblowers to sort through large volumes of publicly available data. While these technologies offer powerful tools for uncovering complex patterns, they also pose a threat to healthcare companies due to its employment by professional whistleblowing companies utilizing it to bring qui tam cases based on publicly available Medicare data absent any “insider” knowledge. Companies defending against qui tam cases must navigate these challenges effectively, leveraging the public disclosure bar and Federal Rules of Civil Procedure (“FRCP”) Rule 9(b) to scrutinize and challenge allegations based on AI and data mining.

Challenges Presented by AI and Data Analytics

AI and data analytics have revolutionized the ability to analyze vast datasets and detect potential fraud schemes. However, relying solely on these technologies as the basis for qui tam allegations introduces several challenges:

1. Public Disclosure Bar: The public disclosure bar, codified under 31 U.S.C. § 3730(e)(4), restricts qui tam actions based on information that has already been publicly disclosed. The rationale is to prevent lawsuits that merely recycle publicly available information without adding substantial new insights or original sources of information. This legal provision aims to ensure that whistleblowers bring valuable, novel information to light, rather than exploiting readily accessible data.
2. Specificity in Allegations: The FCA also demands that allegations of fraud be stated with particularity, as per Federal Rules of Civil Procedure Rule 9(b). AI-generated findings may present complex data analyses, but translating these insights into specific, legally sufficient allegations can be challenging without additional corroborating evidence or context.

Impact on Data-Mining Relators

Despite these procedural guardrails, data-mining relators appear undeterred in their whistleblowing efforts. Perhaps at the forefront is Integra Med Analytics (“Integra”), which holds itself out as providing “expert analysis to tackle fraud, waste, and abuse in healthcare.” Integra initially faced challenges before the United States Courts of Appeals for the Fifth and Ninth Circuits, which dismissed Integra’s reliance on statistical anomalies alone to prove fraud.¹ But in the United States District Court for the Southern District of New York, Integra scored its first big win by obtaining government intervention in an FCA case against a group of skilled nursing facilities based in New York. Litigation in that case remains ongoing.

Following Integra’s lead, professional relators are now combing through publicly available COVID-relief fund data to assert similar claims. For example, in United States ex rel. Sidesolve v. Empire Roofing, Inc., et al., No. 3:22-CV-2060-B (N.D. Tex.), a professional whistleblower with no relation to the defendants alleged that a commercial roofing contractor and its network of roofing and disposal companies violated the FCA by allegedly submitting false certifications to receive loans through the Paycheck Protection Program. Like Integra, SideSolve relied on publicly available data to bring its case. Ultimately, the government secured a $9 million settlement, with a sizeable portion attributed to the professional relator. There, the relator seemingly circumvented the public disclosure bar and potential pleading obstacles by securing an early settlement.

Incentivized by a potential windfall in recovery, professional whistleblowers remain incentivized to continue employing data mining to bring their claims. But as the range of potential whistleblowers grows, litigants must continue to be vigilant in asserting defenses based on who has standing to bring these claims.

[1] See generally U.S. ex rel. Integra Med Analytics, L.L.C. v. Baylor Scott & White Health, et al., No. 19-50818 (5th Cir. May 28, 2020); Integra Med Analytics, L.L.C. v. Providence Health Servs., No. 19-56367 (9th Cir. March 31, 2021).