When the Centers for Medicare & Medicaid Services (CMS) announced its vision to modernize Medicare program integrity, Administrator Seema Verma highlighted the agency’s interest in seeking new innovative strategies involving machine learning and artificial intelligence.
Executive Order Directs HHS to use AI to Detect Fraud and Abuse
The announcement came earlier this month and followed an Executive Order by President Trump which urged the Secretary of Health and Human Services (HHS) to direct “public and private resources toward detecting and preventing fraud, waste, and abuse, including through the use of the latest technologies such as artificial intelligence.”
Medicare Fraud Estimated between $21 and $71 Billion Annually
Medicare fraud, waste, and abuse costs CMS and taxpayers billions of dollars.
In 2018, improper payments represented five percent of the total $616.8 billion of Medicare’s net costs. And it is estimated that Medicare loses between $21 and $71 billion per year to fraud, waste and abuse.
Part of those costs are driven by inefficiencies in trying to identify and flag these issues before, during and after they occur.
For example, today, clinicians manually review medical records associated with Medicare claims and as a result, CMS reviews less than one percent of those records.
Artificial intelligence and machine learning could be more cost effective and less burdensome, and can help existing predictive systems designed to flag fraud.
HHS Among Largest Data Producers in the World
In order to understand the potential for AI, CMS also recently issued a Request for Information asking, among other things, if AI tools are being used in the private sector to detect fraud and how AI can enhance program integrity efforts.
HHS, which houses CMS, is among the largest data producers in the world, with its healthcare and financial data exceeding petabytes per year, making it the perfect fit for AI and machine learning models.
In fact, researchers at Florida Atlantic University programmed computers to predict, classify and flag potentially fraudulent Medicare Part B claims from 2012-2015, using algorithms to detect patterns of fraud in publicly available CMS data. The researchers noted they had only “scratched the surface” and planned further trials.
Just “Scratching the Surface”
But the promise of AI isn’t in just in the CMS data. It’s also in the behaviors of those looking to commit fraud.
According to Jeremy Clopton, director at accounting consultancy Upstream Academy and an Association of Certified Fraud Examiners faculty member, the risk of fraud is often described as having three key factors: a perceived pressure or financial need, a perceived opportunity, and a rationalization of the behavior.
To prevent fraud, AI must analyze behavioral data that might indicate the pressure someone is facing and how they could rationalize fraud to deal with those pressures. For example, he notes that someone facing financial pressures might regularly search for articles related to debt relief and could also mention those concerns in emails. AI has made finding these behaviors more efficient.
AI, Fraud Detection and the Private Sector
The private sector is already embracing AI for a variety of fraud prevention needs. Aetna has 350 machine learning models focused on preventing criminals from fabricating health insurance claims.
And, Mastercard Healthcare Solutions recently announced it would also use AI to identify suspicious activity and help its clients detect fraud .
Beyond just healthcare, the use of AI and ML as part of an organization’s anti-fraud programs is expected to almost triple in the next two years, according to the Association of Certified Fraud Examiners.
And, 55 percent of organizations expect to increase their budgets for anti-fraud technology over the next two years.
Based on the efforts at HHS and CMS, it looks like the Federal Government will be part of the AI-fueled anti-fraud movement.
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