Keith J. Gray’s conviction underscores rising enforcement and audit risk as Medicare spending shifts toward high-cost genetic testing.
A federal jury in Dallas has convicted Texas laboratory owner and former NFL player Keith J. Gray for orchestrating a $328 million Medicare fraud scheme tied to unnecessary cardiovascular genetic testing. Gray, age 39, owned and operated Axis Professional Labs LLC and Kingdom Health Laboratory LLC, which billed Medicare for tests that were not medically necessary, according to the US Department of Justice (DOJ).
The jury convicted Gray on multiple counts, including conspiracy to defraud the United States, violations of the Anti-Kickback Statute, and money laundering. He now faces up to 10 years in prison for each count, with sentencing to be determined by a federal judge.
Gray briefly pursued a professional football career after playing at the University of Connecticut, signing as an undrafted free agent with the Carolina Panthers in 2009 and later spending time on the Indianapolis Colts practice squad, though he never appeared in a regular-season NFL game, according to Fox Sports.
Kickbacks and Sham Contracts Drove Genetic Testing Fraud
According to evidence presented at trial, Gray paid illegal kickbacks to marketers in exchange for Medicare beneficiaries’ DNA samples, personal information, and signed physician orders, the DOJ said. These marketers relied on aggressive telemarketing tactics and a practice known as “doctor chasing,” in which they identified patients’ primary care physicians and pressured them to approve genetic testing orders, prosecutors noted. In many cases, these approvals were based on pre-screening conducted by non-medical personnel rather than legitimate clinical evaluations.
To conceal the scheme, Gray used sham contracts and falsified invoices that were labeled as payments for marketing services, software, or loans. In reality, these payments were structured to match per-sample kickbacks. “Evidence at trial included text messages between Gray and his co-conspirator becoming giddy over the amount of money they were making from Medicare,” the DOJ noted.
Photo credit: NFL Photos
The two laboratories billed Medicare approximately $328 million in fraudulent claims, resulting in about $54 million in payments. Gray used some of these proceeds to purchase luxury vehicles, including high-end trucks and SUVs, as part of efforts to launder the illicit funds.
The case was investigated by multiple federal and state agencies, including the FBI, HHS Office of Inspector General, Texas Medicaid Fraud Control Unit, and the VA Office of Inspector General, underscoring ongoing enforcement efforts targeting fraud in clinical laboratory testing.
The Gray case underscores exactly the risk highlighted in a recent article from The Dark Report on a report from the Department of Health and Human Services’ Office of Inspector General that found genetic tests make up just 5% of volume but now drive 43% of Medicare Part B lab spending. As Medicare spending becomes increasingly concentrated in high-cost genetic testing, enforcement agencies are intensifying scrutiny around medical necessity and billing practices.
Gray’s $328 million fraud scheme—built on kickbacks, questionable ordering practices, and medically unnecessary tests—reflects the same misbehaviors regulators are now targeting. Because of fraud cases such as this, honest laboratories must make greater effort to strengthen compliance, validate ordering patterns, and prepare for heightened audits in the molecular diagnostics space.
Strategies to mitigate diagnostic testing fraud will be a key focus at the 31st Annual Executive War College taking place in New Orleans April 28-29.
With genetic test spending under the microscope, CMS is asking labs to help shape future anti-fraud rules—before formal proposals are drafted.
Clinical laboratory leaders have a narrowing window to influence a major new federal anti-fraud initiative, as the Centers for Medicare and Medicaid Services (CMS) closes its March 30 comment period on its “CRUSH” request for information (RFI).
Published Feb. 27, the CRUSH (Comprehensive Regulations to Uncover Suspicious Healthcare) initiative signals CMS’s intent to pursue broader regulatory changes aimed at strengthening program integrity across Medicare, Medicaid, and Medicare Advantage. For laboratories, the RFI offers an early opportunity to weigh in before formal rulemaking begins.
CMS Targets Genetic Testing Spend, MolDX Oversight, and Advanced Analytics in Fraud Crackdown
A central focus is fraud tied to clinical diagnostic testing, particularly genetic and molecular assays. CMS cited recent federal data showing that while genetic tests accounted for just 5% of Medicare Part B test volume in 2024, they represented 43% of total lab spending—approximately $3.6 billion. The agency also pointed to ongoing enforcement actions and fraud alerts tied to laboratory testing as justification for heightened scrutiny.
Photo credit: Wikimedia Commons
CMS is asking laboratories and other stakeholders to identify what new regulatory authorities, analytics tools, and data-driven approaches could improve detection and prevention of fraudulent billing. The agency’s questions suggest interest in expanding pre- and post-payment review, strengthening data analytics, and accelerating fraud detection earlier in the reimbursement claims lifecycle.
Another key issue for labs is CMS’s examination of the Molecular Diagnostic Services (MolDX) program. The agency is seeking feedback on whether MolDX registration reduces fraud risk and why some payers require participation even outside MolDX jurisdictions—raising the possibility of broader adoption or new requirements tied to molecular test oversight.
For laboratory executives, compliance leaders, and revenue cycle teams, the implications are significant. The RFI signals where CMS may tighten oversight next, particularly for high-cost, high-complexity testing. Policies affecting enrollment, documentation, payment review, and test validation could follow.
With the March 30 deadline approaching, laboratories still have time to submit operational insights and data to help shape future regulations.
Experts share strategies to help clinical laboratories prepare for disruptions, protect samples, and maintain testing operations during unexpected crises.
Laboratory crises rarely announce themselves in advance. They may begin with an after-hours phone call, a freezer alarm that fails to trigger, or a system outage that forces leaders to act before all the facts are known. In clinical laboratories, a crisis is not limited to catastrophic accidents. It can include any event that disrupts regulated operations or threatens staff safety—from equipment failures and power outages to cyber incidents, water damage, or supply chain breakdowns. For laboratory leaders, the central question is not whether disruptions will occur, but whether the lab is prepared when routine safeguards fail.
Effective preparation begins by identifying where failures could cascade across laboratory operations. Many labs track hazards, but fewer examine how a single breakdown could ripple through staffing, equipment, utilities, vendors, and data systems.
Jason Nagy, PhD, MLS (ASCP), lab safety support coordinator for Sentara Health, recommended starting with the earliest point of failure and working backward to identify mitigation steps. In practice, this type of analysis often reveals a common issue: staff uncertainty during emergencies. Written procedures alone rarely prepare laboratorians to respond under pressure, making drills and scenario-based training essential. (Photo credit: Sentra Health)
Cross-training is another critical safeguard. When only a few individuals know how to manage spill responses, downtime procedures, or emergency shutdowns, those employees quickly become overwhelmed while others hesitate to act.
Systems, Communication, and Leadership
Infrastructure reliability is another major factor in crisis resilience. Critical systems—including alarm monitoring, backup power, and environmental controls—must be tested regularly to ensure they function when staff are offsite. Durnan noted that many laboratories discover alarm failures only after equipment losses occur, such as freezer systems that fail over a weekend without notifying staff.
Supply redundancy can also determine whether labs preserve irreplaceable materials. During a building flood that disrupted liquid nitrogen deliveries, Durnan’s lab avoided sample loss because a backup supply tank was already in place.
When disruptions occur, leadership coordination becomes essential. Nagy described how Sentara Health activates an incident command center during emergencies, bringing together couriers, receiving labs, and leadership to quickly coordinate decisions such as specimen rerouting and operational adjustments.
Even with preparation, uncertainty remains inevitable. Nagy emphasized the importance of contingency planning, noting that laboratories should always have multiple fallback strategies when normal workflows break down.
For clinical laboratory leaders, the broader takeaway is that resilience must be built into everyday operations. Training, infrastructure testing, cross-training, and well-defined communication structures help ensure laboratories can protect staff, preserve samples, and maintain testing services when unexpected disruptions occur.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
New digital assessment helps lab leaders identify systemic safety gaps before serious injuries and fatalities occur.
Dark Daily’s sister publication, Lab Manager, recently reported that the National Safety Council (NSC) has launched a new digital assessment tool designed to help laboratories identify systemic safety weaknesses before they lead to serious injuries and fatalities—high-consequence events that can carry significant human and financial costs.
Called the Organization Safety Gap Analysis Tool, the platform adapts NSC’s evidence-based SIF Prevention Model into an interactive, structured evaluation tailored to complex work environments such as clinical laboratories. The initiative was developed through NSC’s Work to Zero program in partnership with the NCCCO Foundation.
For clinical laboratories that already operate on tight staffing models, the NSC tool may help identify areas for investment that will keep workers safer.
Moving Beyond Compliance to Prevent High-Severity Laboratory Incidents
Serious injuries and fatalities (SIFs) are rare but catastrophic events that result in life-altering harm or death. In laboratory settings, they can arise from chemical exposures, fires, equipment malfunctions, uncontrolled energy releases, or containment failures. Unlike minor injuries, SIFs typically emerge from organizational and system-level breakdowns rather than a single unsafe act—making them difficult to detect through conventional safety audits.
Traditional compliance reviews often focus on lagging indicators such as recordable injury rates and incident counts. While those metrics remain important, they do not necessarily reveal whether safety systems are strong enough to prevent high-severity events. The new SIF prevention tool shifts the emphasis from counting past incidents to evaluating whether leadership practices, hazard identification processes, and control systems are capable of preventing catastrophic outcomes.
The digital assessment can be completed in approximately 10 to 15 minutes. Laboratory leaders respond to a series of statements using a color-coded scoring system—green for full compliance, yellow for partial compliance, and red for limited or no evidence of compliance. The platform then generates a customized summary highlighting strengths, identifying safety gaps, and offering targeted recommendations aligned with best practices.
The tool evaluates performance across seven core elements that NSC identifies as critical to preventing serious injuries and fatalities: the safety and health operating environment; management leadership; worker engagement; hazard identification and prioritization; hazard abatement and control; implementation and operation; and continuous improvement.
High-Severity Risk is a Business Risk
Together, these elements are designed to uncover systemic vulnerabilities that may not surface during routine inspections or regulatory compliance reviews.
For laboratories, the business implications extend well beyond worker safety. Clinical labs routinely handle volatile chemicals, compressed gases, biological agents, cryogenic systems, and high-energy equipment. While most facilities meet baseline regulatory requirements, catastrophic incidents often occur when multiple small failures align—failures that may go unnoticed without a structured, system-level evaluation.
A single serious event can result in operational shutdowns, regulatory scrutiny, liability exposure, reputational damage, and increased insurance costs. As accrediting bodies and regulators place greater emphasis on high-severity risk prevention, laboratory leaders are under increasing pressure to demonstrate that their safety programs are proactive, data-driven, and capable of controlling enterprise-level risk.
By benchmarking safety maturity and pinpointing gaps in leadership alignment, hazard prioritization, and control effectiveness, the SIF prevention tool offers laboratory managers a framework for more strategic decision-making. Results can inform investments in engineering controls, workforce training, operational safeguards, and internal audit processes.
As laboratory environments grow more complex and regulatory expectations continue to evolve, industry observers note that organizations can no longer rely solely on compliance-based approaches. Systematic prevention of high-consequence events is becoming a core component of sustainable laboratory operations—and a critical safeguard for both people and business continuity.
Until then, clinical laboratory professionals must push the proposed legislation forward to achieve PAMA reform.
Officials from one of the key groups behind the proposed RESULTS Act stated earlier this month that the goal is to have the legislation attached to a year-end spending bill in Congress.
That leaves approximately 10 months for the clinical laboratory industry to mount enough momentum to bring the proposal to a vote.
“It is never easy to get anything done on Capitol Hill,” noted Joyce Gresko, legal counsel for the American Clinical Laboratory Association (ACLA) and an attorney at Alston & Bird.
Gresko spoke during a Feb. 11 webinar hosted by ACLA about the current lab operating environment under the Protecting Access to Medicare Act of 2014 (PAMA). PAMA-related cuts to clinical lab test reimbursement rates have been delayed until Jan. 1, 2027. However, in the nearer term, an important reporting milestone under PAMA begins on May 1.
Joyce Gresko, legal counsel for the ACLA and an attorney at Alston & Bird, urged clinical laboratories to contact members of Congress about passing the RESULTS Act. (Photo credit: Alston & Bird)
The RESULTS Act—more formally the Reforming and Enhancing Sustainable Updates to Laboratory Testing Services Act of 2025—calls for PAMA reform by permanently capping reimbursement cuts to 5% annually and identifying an independent claims database to help the federal government set Medicare rates for lab test claims. Currently those rates are set through lab-based reporting, an approach that has been largely criticized by the diagnostics industry.
Gresko noted that ACLA is eyeing the idea of the RESULTS Act becoming part of an end-of-year spending package in December, which could allow the proposal to pass as part of a larger vote. Such packages are typical in Congress.
Lawmakers Need to Hear about Support for the RESULTS Act
The ACLA has been among the loudest voices in the clinical laboratory industry supporting passage of the RESULTS Act. The bill was introduced in September 2025, as reported by Dark Daily.
Through a special ACLA website, StopLabCuts.org, 190,000 messages have been sent to Congress from lab industry professionals, Gresko said. She urged others to let their opinions be heard by lawmakers.
“Please weigh in with your members of Congress,” she said.
PAMA cuts have costs the diagnostics industry $3.8 billion over the last decade, noted Susan Van Meter, president of the ACLA.
For labs, those cuts “have had a negative impact on being able to maintain access to a whole level of [diagnostic] services,” Van Meter added.
New PAMA Reporting Window Starts on May 1 for Clinical Labs
While the RESULTS Act gets debated, clinical laboratories will need to prepare for their next reporting window under PAMA, which begins on May 1 and ends on July 31.
PAMA requires affected labs to submit information about tests they perform to the Centers for Medicare and Medicaid Services, including what private payers reimbursed labs for each test. This data establishes Medicare reimbursement rates under the Clinical Laboratory Fee Schedule.
In a twist, Congress revised that data collection to include 2025 commercial rates for labs, not 2019 data as was originally mandated.
Laboratories that have not started preparing for this reporting window need to begin now.
The association urges stronger regulations and data standards to keep AI safe and fair in clinical laboratories.
The Association for Diagnostics & Laboratory Medicine (ADLM) is calling on Congress and federal regulators to modernize laboratory oversight as artificial intelligence (AI) becomes more embedded in clinical testing, warning that without updated safeguards, AI tools could put patients—particularly those from historically marginalized groups—at risk.
In a recently released position statement, ADLM cautions that while AI has the potential to improve diagnostic accuracy, streamline laboratory workflows, and strengthen data-driven decision-making, poorly governed systems could amplify bias and undermine patient safety. For laboratory professionals evaluating or deploying AI-enabled tools, the message is clear: Oversight, validation, and data integrity must keep pace with innovation.
Bias Risks Drive Push to Update CLIA and Standardize AI Oversight in Clinical Labs
AI models are only as reliable as the data used to train them, the organization noted. When systems are built on limited, inconsistent, or historically skewed datasets, they may replicate societal inequities. In healthcare, that can translate into underestimating disease risk or misclassifying conditions in racial and ethnic minorities, older adults, and underserved populations. Because many AI health tools rely on historical datasets that underrepresent certain groups, laboratories could unknowingly implement algorithms that perform unevenly across patient demographics.
To address those risks, ADLM is urging federal policymakers to explicitly incorporate AI systems into existing laboratory regulations, including updates to the Clinical Laboratory Improvement Amendments (CLIA). The group also recommends that federal health agencies work with professional societies to convene laboratory medicine and informatics experts to establish consensus guidelines for validating and verifying AI tools used in test interpretation and clinical decision support.
As reported on in 2025 by The Dark Report, the call comes in the wake of the federal government’s decision last year to eliminate the Clinical Laboratory Improvement Advisory Committee (CLIAC), a key advisory body to CMS and CDC that could have served as a natural forum for advancing ADLM’s proposed updates to CLIA to address artificial intelligence oversight.
Clinical Labs Push for Data Standards and Clear AI Accountability
In addition, ADLM is calling for expanded federal efforts to harmonize laboratory test results and standardize data reporting. Foundational steps, the organization argues, for reducing variability that can compromise algorithm performance. The statement also presses AI developers to increase data diversity, minimize bias in training datasets, and ensure laboratories have access to the technical information needed to independently evaluate algorithm performance.
“Clinical laboratories are uniquely positioned to help develop and assess the integration of AI health tools into testing workflows and, most importantly, how they influence patient test results and health outcomes,” said ADLM President Paul J. Jannetto, PhD.
Jannetto added, “We therefore urge the federal government to draw on the expertise of laboratory medicine professionals in order to develop AI regulations that support innovation, as well as transparent, consistent performance monitoring of this potentially revolutionary technology.” (Photo credit: ADLM)
For lab executives and medical directors, the position statement reinforces a growing reality: AI governance is quickly becoming a core operational and compliance issue. As adoption accelerates, laboratories may face heightened expectations from regulators, payers, and health system partners to demonstrate that AI-driven tools are analytically sound, clinically validated, and equitable across patient populations.
