Analysis Group Authors Explore How Data Are Reshaping Health Care Fraud Litigation
October 23, 2025
As government agencies and courts increasingly rely on data-driven tools and AI technologies to detect fraud, they must decide how to evaluate the reliability of real-world data (RWD) as evidence. However, without economic expertise to address the practical use of such data, even the most advanced algorithms can improperly cast certain medical practices as fraudulent. Pairing quantitative research with contextual understanding can help officials and factfinders reach more balanced and accurate conclusions about allegations of fraud in litigation.
To explore this topic, Analysis Group Managing Principal Andrée-Anne Fournier and Vice President Atang Gilika coauthored an article in Law360 with Jaime L.M. Jones of Sidley Austin. The piece explores how the growing use of AI and data analytics in health care fraud enforcement is prompting courts to consider how best to evaluate RWD. The article also examines data-mining methods that may be used to detect potential fraud, the challenges of interpreting patterns without context, and how courts have approached these issues in recent cases. The authors conclude that these tools may be most effective when used alongside theoretical grounding, clinical expertise, and institutional insight.
The article, "How Courts May Interpret Data-Driven Healthcare Fraud Suits," was published in Law360.