Over the past decade, the U.S. Department of Justice (the "Department" or "DOJ") has increased its usage of data in its investigations either by utilizing billing data to identify outliers in the healthcare context or using data to demonstrate the relevant alleged false statements, claims or other misrepresentations. In either circumstance, data compilation, analysis, and review may be used by attorneys on both sides of the aisle to identify potential fraudulent conduct (in-house), bolster whistleblower cases/ complaints (whistleblower attorneys/government), and/or defend against allegations of fraudulent and false statements under the False Claims Act ("FCA") or other relevant fraud statutes.
How to identify there is a problem as a company
For an in-house attorney, one often associates the utilization of data in the FCA context with defending a FCA investigation or case and not from a more preemptive stance. In reality, however, data—whether from medical billing information or information compiled from customer complaints or testing—can be used to proactively identify issues, perform a root cause analysis (if needed) and remediate issues prior to a potential whistleblower case. In fact, by compiling and analyzing the relevant information, an in-house attorney, compliance professional or outside counsel can replicate the same type of analysis that the government would engage in if the company were under investigation. By doing so, the company could, potentially, avoid exposure altogether by remediating the issue or conducting an internal investigation and documenting the company's investigative approach and the suggested remedial action, if any. But, in order accomplish this and effectively dodge a potential qui tam, first, the company must actually have controls and other systems in place which monitor and observe the frequency of certain outliers. In theory, all companies have the capability of monitoring such data because such data exists at all companies—whether hospital or defense contractor—but the fundamental question is whether the company has set up the appropriate processes and systems to identify, monitor, and analyze data related to key areas where risk of fraud is heightened. At base, in order to identify that a problem and remediate it, the company must have the information necessary to appreciate a potential issue exists.
More recently, in March 2023, the Department updated the Justice Manual, a comprehensive publicly-available manual that sets forth internal DOJ policies and procedures, stressing the importance of an effective compliance program. Specifically, the Justice Manual's "Federal Prosecution of Business Organizations" describes specific factors that prosecutors should consider in conducting an investigation of a corporation, determining whether to bring charges, and negotiating plea or other agreements. See JM 9-28.300. These factors include, among others, (i) the adequacy and effectiveness of the corporation's compliance program at the time of the offense, and (ii) the corporation's remedial efforts to implement an adequate and effective corporate compliance program. Although this guidance applies to active investigations and charging decisions, it is nonetheless valuable in demonstrating the importance of an effective compliance program and its role in identifying and evaluating risk and the prevalence of misconduct. Specifically, in evaluating the quality and effectiveness of a compliance program, prosecutors consider the following questions:
- What methodology has the company used to identify, analyze, and address the particular risks it faces?
- What information or metrics has the company collected and used to help detect the type of misconduct in question?
- How have the information or metrics informed the company's compliance program?
In other words, the same questions that prosecutors consider in evaluating the quality of a compliance program and charging decisions are relevant when identifying and remediating issues preemptively.
How whistleblowers and the Government utilize data analytics to identify fraudsters
Similarly, the Government is increasingly using data analytics to identify potential fraudsters. At the FBA Qui Tam Conference in February 2021, Acting Assistant Attorney General Brian M. Boyton noted the use of "sophisticated data analytics . . . to identify patterns across different types of health care providers" and "identify trends and extreme outliers."1 Indeed, the Government can and does routinely utilize data analytics and trends to identify potentially "high risk" physicians and companies and estimate the cost of their outlier conduct to the federal healthcare programs.2
The Government's use of data analytics in the healthcare context is widely known in amongst the defense and whistleblower bars, but, notably, the Department has stressed its "plans to rely on the use of this analysis to combat other types of frauds, including COVID-19 related misconduct" where available. As discussed below, in the non-healthcare context, the Government regularly utilizes data analytics in building its cases, but its use of data analytics in identifying potential fraudsters has been less widespread outside of healthcare. Particularly, in the procurement and financial fraud context, the Government encounters more barriers to entry and fewer opportunities to obtain meaningful data for the purpose of observing trends and outliers, all of which thwarts the Government's efforts to identify non-healthcare fraudsters. Given these challenges, this is an area where whistleblowers could potentially supplement the Government's efforts and provide additional insight where the Government's access to such data and information is more limited.
For example, in United States ex rel. Mandel v. Sakr, 17-cv-907 (W.D. N.Y. 2022), the relator used data analytics to identify what the physician was not doing. In other words, to ferret out "procedures not performed at all or were not documented in patient medical records" but billed to Medicare and Medicaid.3 As stated in paragraphs 76-80, Relator Mandel used a combination of public data issued by the Centers for Medicare and Medicaid Services ("CMS") and coupled it with his professional knowledge to distill the information and connect the data with the fraudulent billing allegations. This use of public information and private knowledge and/or expertise is also commonly utilized in U.S. Securities and Exchange Commission Whistleblower Program submissions after securities laws violations have been detected.
Using Data Analytics in Building/Defending Against a Case
Unsurprisingly, the Government's usage of data analytics in building cases pre-intervention is so prevalent that it is often impossible, if not extremely challenging, to defend a FCA matter without enlisting the use of an expert and engaging in, at least some, data analytics. Whether in the context of healthcare, procurement, or financial fraud cases, data analytics can range from sampling and reviewing medical records to sampling and reviewing testing data for a defense contract. In most instances, statistical sampling is needed to effectively opine on the underlying allegations. In simple terms, sampling allows for the government or the defense to extrapolate or extend an expert's findings to the entirety of the target population.
Post-intervention or at trial, courts have differing views on the efficacy and aptness of sampling in the FCA space. Some courts have allowed "the use of statistical sampling" as a "legally viable mechanism which the Government may employ" when proving FCA claims. See U.S. v. Life Care Centers of America, 114 F. Supp.3d 549, 571-72 (E.D. Tenn. 2014); see also U.S. ex rel. Michaels v. Agape Senior Community, Inc., 2015 WL 3903675, at *6 (D.S.C. June 25, 2015) at *8 (noting that there are cases where statistical sampling represented the only way the plaintiffs-relators could prove damages). Whereas other courts have wholeheartedly rejected the use of statistical sampling to prove damages or liability. See U.S. ex rel Crews v. NCS Healthcare of Illinois, Inc. 460 F.3d 853, 857 (7th Cir. 2006) (rejecting relator's attempt to establish FCA liability based on percentages rather than proof of actual false claims); see also U.S. ex rel. El-Amin V, George Washington Univ., 533 F.Supp.2d 12, 31 N.9 (D.D.C. 2008) (requiring specific evidence of fraud for each individual claim). Though there are cases on both sides of this issue, the Government faces a great deal of litigation risk given many courts view this question largely as factual inquiry in light of that specific case. Indeed, courts are less likely to allow statistical extrapolation when the question at issue turns on clinical or medical judgment. See Agape Senior Community, 2015 WL 3903675, at *8 ("Distilled to its essence, each claim asserted here presents the question of whether certain services furnished to nursing home patients were medically necessary."). Indeed, courts have held that "statistical sampling . . . cannot establish liability for fraud" where the underlying question "is inherently subjective, patient specific, and dependent on the judgment of involved physicians." See U.S. v. Vista Hospice Care, Inc., 2016 WL 3449833, at *11 (N.D. Tex. June 20, 2016). Ironically, despite some courts' reluctance to apply sampling and extrapolation in proving such cases, these are precisely the sort of cases in which the Government frequently utilizes such methods to prove the case.
Indeed, it would prove particularly difficult for the Government demonstrate a consistent proof of misconduct across hundreds, if not thousands, of patients or claims without utilizing these sampling methods. In such instances, the Government or relators might attempt to bolster such claims by increasing the sample size of the review substantially and only pursue cases where the rate of misconduct is decisive. Whereas, counsel may defend against such allegations by continuing to cast doubt on the Government's findings from the review, such as providing a conflicting medical or professional opinion (when appropriate) or providing contemporaneous evidence of the provider's state of mind from her notes, emails or the patient file, when applicable. Doing so will ultimately muddle the waters for the factfinder—as it is after all, the Government's burden of proof to prove fraud under the FCA.
Simply put, although data analytics and review are helpful tools to identify and investigate fraud both within and outside of the healthcare context, ultimately, at trial, depending on the case, the Government may face substantial exposure post-intervention in effectively proving its case. Relators should be thoughtful in alleging cases based solely on data, and, in some circumstances, despite the wealth of resources at the Government's disposal in evaluating data-based claims at the investigative stage, the tides may actually sway in the defense's favor post-intervention and at trial.
Footnotes
1 DOJ, Acting Assistant Attorney General Brian M. Boynton Delivers Remarks at the Federal Bar Association Qui Tam Conference (Feb. 17, 2021), https://www.justice.gov/opa/ speech/acting-assistant-attorney-general-brian-m-boynton-delivers-remarks-federal-bar.
2 Id.
3 DOJ, Dansville Physician Agrees To Pay More Than $600,000 To Resolve Allegations That He Fraudulently Billed Medicare and Medicaid (June 7, 2022), https://www.justice.gov/usao-wdny/pr/dansville-physician-agrees-pay-more-600000-resolve-allegations-he-fraudulently-billed#:~:text=Medicare%20And%20Medicaid-,Dansville%20Physician%20Agrees%20To%20Pay%20More%20 Than%20%24600%2C000%20To%20Resolve,Fraudulently%20Billed%20Medicare%20And%20Medicaid&text=BUFFALO%2C%20N.Y.,–%20U.S.%20Attorney%20Trini%20E.
This is co-authored by Rachel V. Rose, JD, MBA (Houston, Texas), advises clients on compliance, transactions, government administrative actions, and litigation involving healthcare, cybersecurity, corporate and securities law, as well as False Claims Act and Dodd-Frank whistleblower cases.
Originally published by FBA's Qui Tam Section in the Summer 2024 newsletter, Turning Square Corners
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