News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

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Clinical Laboratories and Pathology Groups

Hosted by Robert Michel

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Ex-NFL Player Convicted in $328M Genetic Testing Fraud as Medicare Scrutiny Intensifies

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.

—Janette Wider

Certain States Develop Their Own AI Regulations for Clinical Communications

Recent laws in California, Utah, and Texas define new compliance standards for clinical laboratories employing AI in diagnostic and clinical messaging.

When it comes to oversight of artificial intelligence (AI) use in clinical laboratory, it behooves lab leaders to watch what is happening on the state level. In some cases, disclosure of AI use is a threshold states are monitoring.

For example, California Assembly Bill 3030, which went into effect Jan. 1, 2025, mandates transparency when generative AI is used in healthcare. Any health facility, laboratory, clinic, physician’s office, or group practice that employs generative AI to create patient communications about clinical information must include:

  • A prominent disclaimer stating the content was AI-generated.
  • Clear instructions that inform patients how to speak directly with a human clinician.

If a licensed provider reviews and approves the AI-generated communication, these requirements are waived. AB 3030 applies only to clinical—not administrative—messages. Non‑compliance can result in disciplinary actions from state regulators.

Laboratories using AI in patient-facing contexts should ensure their workflows include AI‑disclaimers, human‑review triggers, and clear ways for patients to contact providers.

“Symposium Cisco Ecole Polytechnique 9-10 April 2018 Artificial Intelligence & Cybersecurity” by Ecole polytechnique / Paris / France is licensed under CC BY-SA 2.0.

AI Disclosure in Utah

Meanwhile, Utah Senate Bill 226 updates its Artificial Intelligence Policy Act, tightening rules around how healthcare entities—including clinical labs—use generative AI in patient interactions. The rules went into effect May 7, 2025.

Under the state’s law, labs must disclose AI use only when:

  • A patient explicitly asks whether they’re interacting with AI, or
  • The lab uses AI in high-risk communications, such as delivering test interpretations, diagnostic results, or clinical advice.

Routine AI use in back-end operations or non-clinical messaging does not require disclosure.

A safe harbor provision protects labs from penalties if the AI system clearly identifies itself as non-human at the beginning and throughout the interaction.

Labs that use AI-generated content in patient portals, chatbots, or outreach must ensure compliance or face consumer protection penalties.

New Texas Law on AI

Texas passed a law in June that goes into effect Sept. 1, 2025, the regulates how AI is used within electronic health records (EHRs).

According to the law, providers that use AI for recommendations on diagnosis or treatment based on a patient’s medical record must review all information obtained through AI to ensure its accuracy before entering the information into a patient’s EHR.

The law also “imposes a strict data localization mandate, prohibiting the physical offshoring of electronic medical records,” law firm Holland & Knight noted. “This requirement applies not only to records stored directly by healthcare providers but also to those maintained by third-party vendors or cloud service providers.”

—Scott Wallask

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