A rapid succession of structural and policy changes in 2025 and 2026 have made data-driven health care fraud enforcement faster, more coordinated, and better resourced than at any point in the history of the Medicare or Medicaid programs. To date, these enforcement efforts largely have been directed at traditional provider operations. Over time, however, these tools also might be used to identify potential fraud in more discrete, context-specific circumstances, such as changes of ownership following a provider acquisition.
A New Enforcement Architecture
Over the past year and a half, health care fraud enforcement has undergone a structural transformation, becoming more centralized, coordinated, and data-driven than ever before. For example:
- On May 12, 2025, DOJ issued a white-collar enforcement plan which, consistent with historical DOJ enforcement trends, placed health care fraud at the top of its priority list. The plan notes that the Criminal Division will prioritize efforts to identify and seize assets related to health care fraud and other high-impact areas.
- On June 30, 2025, DOJ announced the results of an annual “National Fraud Takedown” through which it coordinated with multiple agencies to charge 324 defendants with participating in health care fraud schemes involving over $14.6 billion in false claims submitted to Medicare, Medicaid, and other federal health care programs. DOJ concurrently announced that it was working with the FBI, HHS-OIG, and other federal agencies to create a “Health Care Fraud Data Fusion Center” that would use “coordinated data analysis” to “break down information silos” and “quickly identify and dismantle emerging fraud schemes.”
- On July 2, 2025, DOJ and HHS formed the DOJ-HHS False Claims Act Working Group to “maximize cross-agency collaboration,” including through (i) referrals from HHS to DOJ of potential FCA violations involving six priority health care enforcement areas1 and (ii) “enhanced data mining and assessment of HHS and HHS-OIG report findings.”
- On March 16, 2026, President Trump issued an executive order establishing the Task Force to Eliminate Fraud within the Executive Office of the President. The Task Force is charged with coordinating a comprehensive national strategy to combat fraud, waste, and abuse in federal benefit programs, including health care programs, and directs agencies to adopt prepayment integrity controls and expand intergovernmental data sharing across federal and state systems as anti-fraud mechanisms.
- On April 7, 2026, DOJ created the National Fraud Enforcement Division (NFED) to “consolidat[e]” and “realign[]” resources within the Department in order to “avoid duplication, draw clear lines of effort between divisions, minimize layers of bureaucracy, centralize relevant expertise, and, ultimately, maximize results.” Assistant Attorney General Colin McDonald, who heads the Division, has stated that it will be “interested in all” levels of fraud.
- As we wrote about in a recent Dentons On Call blog post, on April 30, 2026, DOJ launched the FOCUS initiative (Fraud Oversight through Careful Use of Statistics), formalizing DOJ’s working relationship with “data miner” qui tam whistleblowers. That same day, DOJ formed the West Coast Health Care Fraud Strike Force explicitly because “data analytics show[ed]” a “migration of fraud schemes” to that region.
- Finally, and most recently, on June 23, 2026, DOJ announced the results of its 2026 National Health Care Fraud Takedown, in which DOJ charged a record 455 defendants in schemes involving more than $6.5 billion in alleged fraud. The campaign spanned 56 federal districts across 45 states and territories, with 50 state Medicaid Fraud Control Units participating, the most in DOJ’s history. This same announcement highlighted (i) DOJ’s first prosecution arising from the new Health Care Fraud Data Fusion Center’s Financial Intelligence Review Team (summarized below), (ii) an agreement between DOJ and CMS to give DOJ’s Fraud Division cloud-computing space within the CMS Integrated Data Repository to deploy real-time analytics algorithms and AI tools to detect fraud, and (iii) data-sharing agreements between DOJ’s Fraud Division and each of DHS and FTC “aimed at breaking down data silos and improving access to information critical to identifying and combatting healthcare fraud.”
The bottom line is this: The government now has a centralized enforcement structure, dedicated data analytics teams, interagency collaboration, and an explicit White House mandate to use statistical tools to find and prosecute health care fraud proactively.
Government Use of Data Analytics
According to the DOJ Fraud Section’s 2025 Year in Review, the Data Analytics Team within DOJ’s Health Care Fraud Unit completed 2,085 data requests and generated 164 “proactive data referrals” for the Health Care Fraud Unit and US Attorneys’ Offices in 2025. In other words, the government’s own analysts are increasingly identifying potential fraud targets through claims data independently of any insider or other whistleblower complaint. The Health Care Fraud Data Fusion Center exists specifically to scale this capability by combining data specialists from DOJ, HHS-OIG, and the FBI to leverage cloud computing, artificial intelligence, and advanced analytics in identifying potential Medicare, Medicaid, and other health care fraud schemes.
Several recent enforcement actions illustrate how the government’s data-driven approach plays out in practice:
- In June 2026, DOJ charged Daniel Robinson, the owner of a methadone clinic, for an alleged $92 million scheme to fraudulently bill Illinois Medicaid for behavioral health services that were never provided. The defendant allegedly submitted false claims to Medicaid for 500 or more hours of counseling and therapy services per day—more than was possible even if all providers on staff worked 24 hours per day—and diverted the proceeds toward a combination of brokerage accounts, business ventures, real estate, and luxury items. The Data Fusion Center’s analysis identified that patients were hospitalized at other facilities on days that the defendant billed for the services at issue. According to an HHS-OIG press release, the Health Care Fraud Unit opened the investigation within five days of its financial intelligence review and the defendant was arrested less than seven months later at the airport attempting to leave the country.
- In May 2026, a jury convicted Dr. Violetta Mailyan in the Central District of California in connection with a $45 million Medicare fraud scheme involving claims for Botox injections that were never provided or were provided only for cosmetic purposes (and hence were medically unnecessary). DOJ initiated its investigation after the Health Care Fraud Section’s Data Analytics Team showed that Dr. Mailyan, the physician-owner of Healthy Way Medical Center, was paid more by Medicare for Botox injections than any other physician in the United States. No whistleblower was involved.
- In November 2025, Vohra Wound Physicians Management LLC paid $45 million to resolve FCA allegations that it manipulated its EHR system with pre-programmed features that automatically drove higher-level billing codes. Vohra’s billing pattern, in which all debridements were coded as the most intensive and expensive surgical excisional debridements for nearly eight consecutive years, is the type of anomaly that would be visible in claims data, though the government has not publicly identified the specific mechanism that prompted its investigation. The United States filed its complaint without a relator and secured a five-year Corporate Integrity Agreement.
- Operation Gold Rush, part of the 2025 “Takedown,” may be the most dramatic example. Through “proactive data analytics,” the Health Care Fraud Unit’s Data Analytics Team and its partners detected “anomalous billing” from a network of medical supply companies acquired by a transnational criminal organization using straw owners. According to DOJ, the organization used the acquired companies to rapidly submit $10.6 billion in fraudulent health care claims to Medicare for urinary catheters and other DME. The analysis led to the seizure of $27.7 million in alleged fraud proceeds, prevented roughly $4.4 billion in additional fraudulent Medicare payments to the organization, and precipitated charges in what DOJ described as the largest loss amount ever charged in a health care fraud case.
These enforcement actions share a common thread: each involved a billing pattern that was facially visible in claims data. In some instances, the government has confirmed that its Data Analytics Team proactively identified the anomaly; in others, the precise mechanism that prompted the investigation has not been publicly disclosed. What is clear is that in each case the government filed or resolved its case without a relator, the claims profile of the provider’s billing was central to the government’s theory, and the providers lacked a documented compliance baseline that could explain the patterns their billing produced.
Potential Target Areas
To date, the government’s use of data analytics appears to be focused on provider operations. However, as its data analytics capabilities expand and improve, the government may consider targeting new areas, such as potential fraud tied to discrete events, like a health care acquisition.
For example, assume a hospital acquires a primary care group, employs the group’s physicians, compensates them based on their productivity and, within three months, the physicians’ average E/M coding levels skyrocket. Pre- and post-closing billing patterns offer a natural baseline for comparison: the same physicians, treating a similar patient population, suddenly exhibiting different coding or billing behavior. The ownership change provides a “before and after” that could become the basis for further scrutiny. At the same time, it bears emphasizing that the government’s analytics tools identify statistical outliers, not intent. Legitimate post-acquisition billing changes driven by improved documentation, better access to resources, or clinically appropriate utilization of the acquiring system’s service lines can generate the same data signals as fraud.
In all events, providers engaged in health care acquisitions may mitigate the risk of potential follow-on enforcement actions by (i) undertaking a pre-closing review of the target provider’s pre-acquisition coding distribution, referral patterns, and utilization rates (to serve as a “baseline” for monitoring post-acquisition changes); (ii) implementing claims monitoring systems that track these same metrics (e.g., utilization rates) on an ongoing basis, with alerts and documented follow-up when metrics deviate from the baseline by a defined percentage; and (iii) taking corrective action (e.g., reviewing and revising any post-acquisition modifications to physician compensation formulas) if and where appropriate.
Now, more than ever, data analytics is a critical component of effective compliance monitoring. The math simply cannot be ignored. As reported by DOJ earlier this year, FCA settlements and judgments exceeded $6.8 billion in fiscal year 2025, the highest total in the statute’s history, with $5.7 billion tied to health care. The same statistics reflect that a record 1,297 qui tam lawsuits were filed, and 401 new non qui tam investigations were opened. And according to its 2025 Year in Review, DOJ has calculated a return of $106.76 for every dollar spent on health care fraud enforcement. With the NFED centralizing enforcement, the Data Fusion Center applying AI to claims analysis, and the FCA Working Group promoting referrals from HHS to DOJ in several health care enforcement areas, the infrastructure will only expand. As DOJ leadership stated in announcing the 2025 Takedown, it is “the beginning of a new era of aggressive prosecution and data-driven prevention” of health care fraud. The government’s analytics are already running. The question is whether yours are running too.
- The six priority enforcement areas identified by the DOJ-HHS False Claims Act Working Group are: (i) Medicare Advantage; (ii) drug, device or biologics pricing, including arrangements for discounts, rebates, service fees, and formulary placement and price reporting; (iii) barriers to patient access to care, including violations of network adequacy requirements; (iv) kickbacks related to drugs, medical devices, durable medical equipment, and other products paid for by federal healthcare programs; (v) materially defective medical devices that impact patient safety; and (vi) manipulation of Electronic Health Records systems to drive inappropriate utilization of Medicare covered products and services. ↩︎
