Qui Tam Quarterly - Data on Defense: Invalidating FCA Allegations Based on Statistical Sampling and Extrapolation
Statistical sampling and extrapolation have become accepted tools for establishing damages in health care administrative proceedings and False Claims Act (FCA) litigation over the past 30 years.
Key Takeaways
- The government has been dramatically increasing the use of data mining to identify potential health care fraud and data analysis to support liability and damages in False Claims Act cases.
- One of the primary tools the government uses to assert FCA liability and damages based on claims data is the statistical sampling and extrapolation process.
- Understanding the legal standards and challenges to improper use of statistical sampling and extrapolation is critical to defending FCA allegations based on data analysis.
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ABOUT QUI TAM QUARTERLY
Qui Tam Quarterly is a publication authored by members of the health care fraud and abuse team highlighting emerging and pressing issues in health care fraud and abuse, including litigation and governmental investigations involving the False Claims Act, the Stark Law, the Anti-Kickback Statute, and other health care fraud related statutes.
Members of our team are regular contributors to Triage: Timely Conversations for Health Care Professionals, a podcast created by K&L Gates to inform our clients and friends of the firm about the latest developments in health law.
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This publication/newsletter is for informational purposes and does not contain or convey legal advice. The information herein should not be used or relied upon in regard to any particular facts or circumstances without first consulting a lawyer. Any views expressed herein are those of the author(s) and not necessarily those of the law firm's clients.
