DOJ's new Fraud Division named home health a top enforcement priority on August 13. Here's what the memo signals.

On August 13, DOJ Assistant Attorney General Colin M. McDonald issued a memorandum — "The Fraud Division's Enforcement Priorities" — to all personnel of the Department of Justice's National Fraud Enforcement Division (NFED). The memo defines five standing priority areas for the division: (1) public trust and financial integrity, (2) health care, (3) internal revenue, (4) global trade and commerce, and (5) corporate misconduct.

Home health and hospice are named explicitly within the healthcare priority. McDonald wrote: "Home health aide and hospice scams directly impact vulnerable elderly Americans and erode patient care." The division will use data analytics, financial forensics, technology-driven investigations, and cross-agency information sharing to target these schemes. By August 24, the NFED is scaling to approximately 500 attorneys and staff, with continued growth planned across the next two years.

What's different about a formal priority designation

The June 23 National Health Care Fraud Takedown charged 455 defendants and suspended 1,079 providers. The August 4 Philadelphia action charged 19 defendants in Medicaid home care aide schemes and expanded the Strike Force to the Northeast. The Minnesota Takedown in May charged 15 defendants across $90M in alleged Medicaid fraud. These are individual enforcement events — large ones, but temporally bounded.

A formal enforcement-priority memo is structurally different. It sets the institutional framework that will produce the next round of enforcement events, and the round after that. The NFED's five priority areas are the categories into which resources, prosecutors, data analysts, and interagency relationships are organized on a standing basis. When home health appears in a formal priority memo by name — not as a one-off case type, but as a named sector — it means enforcement infrastructure is allocated to it by design. It is not reactive to a single scandal. It does not stop when a particular Takedown ends. Agencies that have been treating the 2026 enforcement surge as a temporary spike can now recalibrate: what we have seen in the past 12 months is the baseline.

How data analytics change the enforcement math

The memo explicitly names data analytics and technology-driven investigations as enforcement force multipliers. This is consistent with what the enforcement actions of the past year already demonstrated. The DOJ's Data Fusion Center — combining Medicare and Medicaid claims data with financial transaction analysis — surfaced the Philadelphia aide billing more than 24 hours in a single calendar day as an arithmetic impossibility through routine data queries, without a tip. Ohio identified 49 home health providers for payment suspension using newly deployed analytics tools authorized by executive order.

A prosecutor count of 500 alone does not generate 10x the enforcement actions. Data infrastructure does. Five hundred prosecutors working with a Data Fusion Center running outlier analysis against the full national claims dataset is a qualitatively different enforcement environment than 500 prosecutors working from tips and manual referrals. The NFED memo makes explicit that the division intends to build this infrastructure as a standing capability.

What your agency should do

1. Run your billing data through the same cross-references the enforcement data systems are running. Service dates during a patient's facility admission. Aide hours that sum above 24 in a single calendar day. Visit frequency outliers relative to your HIPPS group. OASIS functional scores that don't support billed diagnoses. These are not speculative — they are the documented billing patterns the Data Fusion Center queries against the national claims dataset. If they appear in your data, you need to see them before a program integrity contractor does.
2. Audit your EVV exception rate by discipline and service type. Every visit billed without a GPS-verified EVV record is an open documentation gap. The enforcement infrastructure linking billing data to visit-verification records is exactly what "technology-driven investigations" means operationally. Unverified visits are the gap.
3. Review every financial relationship with referral sources. Anti-Kickback safe-harbor analysis applies to every arrangement — formal or informal — with physicians, hospital discharge coordinators, or facility employees who also generate referrals. The August 4 Philadelphia charges and the May 14 Michigan conviction both turned on financial transaction records from payment apps that were subpoenaed directly from the platforms. The transactions were traceable because they were electronic.
4. Confirm your face-to-face documentation and CMS-485 certification chain is in spec. The NFED's healthcare enforcement portfolio covers Medicare and Medicaid billing fraud broadly. Documentation gaps on face-to-face encounters and physician certifications are among the most common triggers for escalation from billing-anomaly review to active investigation.

What we built for this

Carelytic's billing pipeline cross-references every claim date against HETS eligibility status and flags visits where the service date falls within a known facility admission window — the same cross-reference the enforcement data systems run. The EVV module ties GPS check-in data to each visit record before submission; unverified visits surface in the exception report while there is still time to resolve them. The certification workflow requires a documented face-to-face encounter before the plan of care goes to billing, with the certifying provider's NPI validated against active Medicare enrollment. Every record access and modification carries an immutable audit trail with timestamp, user ID, and reason code. In an enforcement environment where a formal DOJ priority memo names data analytics as a force multiplier, the audit trail is not a reporting feature — it is what the agency presents when documentation is requested, and what distinguishes a billing-pattern anomaly from a compliance failure.