AI Solutions for Healthcare
Decoding the “ACGT” of genetic testing referrals 

Deciphering our past and potential future using genetic testing. 

ACGT is the acronym for the four types of bases found in a DNA molecule: adenine (A), cytosine (C), guanine (G), and thymine (T). Genetic code is the term used to define the way the four bases of DNA–the A, C, G, and Ts–are strung together in a way that the cellular machinery, the ribosome, can read them and turn them into a protein.  

Genetic disorders are diseases caused by a change in the DNA sequence away from the normal sequence. Genetic disorders may be caused by different mutations such as a monogenic disorder [single gene], a multifactorial inheritance disorder [multiple genes], damage to the chromosome, a change in the number or structure of an entire chromosome, or by a combination of environmental factors and gene mutations. 

Genetic testing can assist medical practitioners in determining whether an individual has a genetic condition or may develop one in the future. The information gained from genetic testing may help diagnose a genetic disease, support initiation of a treatment, establish a prevention strategy, as well as make major life decisions related to family or career planning.  

Genetic testing growth and recent fraud schemes 

The expense, volume, and development of novel genetic tests has risen annually since the completion of the Human Genome Project. According to the Medicare Part B utilization reports for laboratory procedures, 12 genetic tests were in the Top 100 procedures in 2021. Of interest, the average charge per service for the 12 genetic tests was approximately $800 compared to an average charge per service of around $23 for the remaining 88 laboratory procedures.  

Based on the population needs and increased coverage for genetic testing in Medicaid and commercial healthcare plans, the overall charges and claim volumes for those plan types likely surpass Medicare. Many tests are considered once-in-a-lifetime or once per episode of care, so the repetition of the same or similar genetic tests for a beneficiary in a short period can be a red flag. Additionally, healthcare forecasts for genetic testing predict double-digit revenue growth over the next 5 years and private equity firms have been investing in the genetic testing market space for at least a decade. 

Despite the U.S. Department of Health and Human Services Office of Inspector General [HHS-OIG] issuing a scam alert in 2019 regarding kickbacks paid by recruiters to doctors for approving genetic tests for their patients, hundreds of millions of dollars have been lost to fraud in our Medicare and Medicaid systems. According to the Federal Bureau of Investigation [FBI] and the HHS-OIG, LabSolutions LLC in Georgia, billed $463 million to Medicare for expensive cancer genetic tests from 2016 to 2019. The owner of LabSolutions LLC, Minal Patel, was sentenced to 27 years in prison in August 2023. In July 2023, the Texas Office of Attorney General Medicaid Fraud Control Unit arrested three individuals associated with ApolloMDx and estimated that $142 million in genetic testing fraud.  

How can your health plan decode the “ACGT” of your genetic testing claims traffic?   

Proactive discovery and detection of genetic testing referral patterns can assist health plans and agencies reduce long-term losses. Just as our DNA contains patterns that define and identify who we are, your claims traffic contains patterns that can help you decode genetic testing referral risks and identify potential bad actors.  

A = Associations 

  • Does the ordering / referring provider have an established relationship with the patient? 
  • Does the ordering / referring provider order genetic tests exclusively from a specific laboratory? 

C = Correlated Risk 

  • Does the laboratory perform a frequent combination of the same genetic tests?  
  • Is the specialty of the ordering / referring provider unusual for the genetic tests ordered? 
  • Did your plan receive the genetic testing bills as part of a Medicare Supplement/Medigap claim?  
  • Does the laboratory have the credentials necessary to perform the genetic tests? 
  • Are genetic tests repeated or contain multiple lines with modifier 59, X[EPSU], 91, or 90? 

G = Geography 

  • Are the beneficiaries or members concentrated in a specific geographic area? 
  • Is the laboratory associated with or operating in a shared space related to gene-therapy?  
  • Is the laboratory location a residential address or private mailbox facility? 
  • Is the laboratory out-of-state or across the country from the bulk of the members? 

T = Timeliness 

  • Has the laboratory received payment for a small number of genetic test claims in the past year? 
  • Are the dates of service for the bulk of the claims from a few months to several months ago? 
  • Were the “older services” submitted in bulk on one date? 

How can Fraud Scope help your health plan or health agency identify providers with signals for potential referral schemes? 

Fraud Scope’s pattern-based detection models, such as Outlier Abuse, Suspicious Trends, Time Behavior or Frequent Combinations, identify outlier utilization, such as:  

  • Laboratory providers with higher volume of genetic tests compared to their peers 
  • Recent peaks or increasing trends for genetic testing utilization 
  • Cookie-cutter procedure code billing across all patients 

Fraud Scope’s Query Builder empowers users to explore healthcare intelligence at the line, claim, or aggregate levels and to define criteria specific to their assessment. 

  • Providers with outlier modifier utilization compared to their peers 
  • Laboratory providers with higher units of service compared to their peers 
  • Higher paid per patient values by genetic testing code compared to all peers 

Fraud Scope’s Provider Dashboard identifies new providers from your claims traffic, established providers with a lapse in claim traffic who recently submitted claims, and providers submitting a code that did not exist in their history.  The three modes within the Provider Dashboard provide the users with claim volume intel and other intelligence early warnings related to:  

  • Utilization of genetic testing codes not within the providers prior billing profile 
  • Immediate high-claim traffic identification of genetic testing from new providers 

Fraud Scope’s Association Graph identifies claim relationships between providers by addresses or patients, and then displays provider identifiers, risk scores, and practice intelligence for the user to assess, which includes hyperlinks to the shared claim data.  The Fraud Scope Association Graph toolkit helps the user identify:  

  • Patient sharing across similar practices or related businesses 
  • High concentration of genetic testing referrals from a small group of medical practitioners 

References: 

National Human Genome Research Institute 

https://www.genome.gov/genetics-glossary/acgt

Medicare utilization statistics for Part B (Supplementary Medical Insurance SMI) - 2021 

https://www.cms.gov/files/document/cy-2021-top-100-lab-procedures-ranked-charges.pdf?agree=yes&next=Accept

U.S. Department of Health and Human Services – OIG: Nationwide Genetic Testing Fraud 

https://oig.hhs.gov/newsroom/media-materials/media-materials-nationwide-genetic-testing-fraud

Lab Owner Sentenced for $463M Genetic Testing Scheme 

https://www.justice.gov/opa/pr/lab-owner-sentenced-463m-genetic-testing-scheme

Texas Medicaid Fraud Control Unit Helps Dismantle $142 Million Genetic Testing Fraud Scheme 

https://www.texasattorneygeneral.gov/news/releases/texas-attorney-generals-medicaid-fraud-control-unit-helps-dismantle-142-million-genetic-testing