Taxpayers Win when Whistleblowers Bring Data-Mining Evidence to DOJ

Whistleblowers who present healthcare data analytics (“data-mining”) in their False Claims Act (“FCA”) complaints can add tremendous value to the Government’s investigation of their claims. And with rapid advancements in technology such as predictive artificial intelligence and biometric data collection, and with tech firms like Apple, Google, Amazon and Comcast joining the healthcare industry in earnest, the value of data mining to healthcare fraud investigations will only continue to grow.

False Claims Act defendants like to argue that employees should not be permitted to covertly disclose patient-related healthcare data to the Government in support of their fraud claims. When not attacking whistleblowers, some of these same defendants also like to collect profits through their own disclosure of patient-related healthcare data to third parties.[1] The reality is that patient-related healthcare data will sometimes find its way into the wrong hands, and will sometimes be used for ill purposes. This outcome is particularly inevitable given that the Supreme Court has upheld the First Amendment right of businesses to collect and sell healthcare information relating to individual patients without patients’ or their physicians’ consent. See Sorrell v. IMS Health Inc., 564 U.S. 552 (2011).

But patient-related healthcare data placed in the right hands may also be used as a force for good. This is true whether the data is used to conduct life-saving scientific research or to return defrauded healthcare funds to U.S. taxpayers. At the federal level, both the Department of Health and Human Services’ Office of Inspector General (“HHS OIG”)[2] and the Center for Medicare and Medicaid Services (“CMS”)[3] rely heavily on data analytics as part of their healthcare fraud prosecution efforts. Additionally, since 2017, the Department of Justice’s (“DOJ”) Health Care Fraud Unit Data Analytics Team has equipped U.S. Attorneys’ Offices around the country with, among other things, “customized HCF data analytics training and ongoing case-specific investigation and prosecution assistance.[4]” Since 2013, state Medicaid Fraud Control Units have also been authorized to use federal funds to conduct data-mining activities to support their local Medicaid fraud investigations. See 78 Fed. Reg. 298055 (May 17, 2013).

The False Claims Act exists because, despite these efforts, the Government cannot do it alone. See U.S. ex rel LaValley v. First Nat. Bank of Bos., 707 F. Supp. 1351, 1355 (D.Mass. 1988) (“The legislative history in both houses of Congress reveals a sense that fraud against the Government was apparently so rampant and difficult to identify that the Government could use all the help it could get from private citizens with knowledge of fraud.”). In the case of healthcare fraud, as CMS has recently acknowledged, “[t]he reality is that the old fashioned way of approaching program integrity has only allowed [CMS] to review less than two-tenths of a percent of the over 1 billion claims that Medicare processes every year…. [W]e are looking at how we can use advanced data analytics and new tools to help us review more claims and address high fraud areas.”[5]

There can be little doubt that, in the age of Big Data, the data-mining whistleblower is healthcare fraud's enemy and the taxpayer's friend. While there are countless examples of FCA cases in which data-mining evidence provided by a whistleblower contributes to recovery, two cases, in particular, show how data-mining, when combined with corroborative evidence of fraud, may be leveraged to prosecute frauds carried out by multiple defendants, or even industry-wide. See e.g. U.S. ex rel. Ford et al. v. Abbott Northwestern et al, Case No. 08-cv-20071 (S.D. Fla.) ($280 million intervened settlement of claims originated by a data-mining whistleblower against more than 500 hospitals alleging medically unnecessary cardiac device implantations); and United States ex rel. James Doghramji; Sheree Cook; and Rachel Bryant v. Community Health Systems Inc., et al., Case No. 3-11-cv-00442 (M.D. Tenn.) ($98 million intervened settlement of claims originated in part by a data-mining whistleblower against 119 hospitals alleging medically unnecessary inpatient hospital stays).

In a landmark ruling allowing the use of statistical data sampling methods to prove liability in a qui tam case, the court described the role of the FCA statute in the modern healthcare fraud landscape this way:

"Over time, the Medicare program has grown, dramatically changing the breadth of the landscape from which false claims may arise. Unlike when the FCA was originally enacted in the 1800s, those who commit fraud today have the aid of tools of technology and a relative unlikelihood of detection deriving from the sheer scale of the Medicare program itself." United States v. Life Care Centers of Am., Inc., 114 F. Supp. 3d 549, 571 (E.D. Tenn. 2014).

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[1] In a recent example, The New York Times reported in December 2018 that Memorial Sloan Kettering Cancer Center (MSKC) entered into a controversial patient-data sharing deal with a for-profit artificial intelligence healthcare startup that was plagued with conflicts of interest.

[2] See https://www.fiercehealthcare.com/aca/oig-budget-data-analytics

[3] See https://www.fiercehealthcare.com/antifraud/cms-data-analytics-gao-oig-fraud-prevention-system-improper-payments

[4] See https://www.justice.gov/criminal-fraud/file/1026996/download at 10.

[5] See https://www.mcknights.com/news/providers-should-expect-new-set-of-quality-measures-more-sophisticated-enforcement-strategies-cms-head-says/?fbclid=IwAR1Lt02BoIa3OY00Cj0omjVxwvV2aoHZDjZEgIqWUbhuysK_6JxvGG93EVQ