Medicare Agency Turns to A.I. to Stop Fraud and Faulty Payments

The Centers for Medicare and Medicaid Services is requesting input from industry on its plan to incorporate advanced technologies such as artificial intelligence into efforts to combat medical fraud and improper payments, according to an Oct. 21 request for information.

CMS wants to understand how AI and other emerging technologies can help it ensure proper claims payment, reduce the burden on health care providers, and conduct program integrity activities more efficiently.

“CMS must work every day to guarantee that we are an accountable steward of Medicare and Medicaid program dollars,” according to the CMS RFI. “Accomplishing this is one of CMS’ top priorities.”

Preventing fraud and improper payments isn’t just a CMS priority — it’s a governmentwide priority. The White House lists “Getting Payments Right” as one of a handful of Cross-Agency Priority (CAP) goals, with officials from the departments of Labor, Treasury, Health and Human Services, and the Office of Management and Budget leading the effort. The White House began posting metrics for reducing improper payments on in 2010 to hold government officials accountable for progress.

In recent years, CMS has taken steps to expand its use of automated program integrity tools and data sharing, and stepped up manual reviews conducted by medical professionals. According to the CMS report on Congress on its fiscal 2017 activities, the latest report available, the agency saved an estimated $15.5 billion by reducing improper payments.

Nevertheless, CMS officials acknowledge that status quo efforts can only go so far, citing a need to move beyond current capabilities, where “new strategies, tools and technologies can help us ensure we remain on the cutting edge of data innovation,” according to the RFI. AI technologies can help CMS’s Center for Program Integrity correlate data across disparate legacy data systems and repositories, the RFI continued.

The RFI invites input from technology companies and healthcare providers on some of the following questions:

  • Do AI tools exist that can read and review medical records and evaluate their compliance with a set of coverage guidelines?
  • Can current AI tools enable the review of more claims without increasing provider burden?
  • What AI tools are already in use in the private sector? Can they detect fraud?
  • What metrics can best assess performance of AI-enabled tools?
  • Are there any other ways in which AI can enhance CMS’s program integrity efforts?

Although CMS has not released an official ceiling value, based on the scope of services required, Bloomberg Government projects that this opportunity could generate $50 million to $100 million for both AI vendors and health care providers over the next five years. The bench of AI vendors may include one or more of the 58 companies that won spots on the Department of Health and Human Services’s $49 million Intelligent Automation and Artificial Intelligence (IAAI) contract, awarded in June 2019.

A quick search using Bloomberg Government’s Contracts Intelligence Tool identified more than $375 million in governmentwide spending on fraud prevention and program integrity contracts in fiscal 2019. CMS represented the greatest share of that spending, with $289 million in obligations, followed by the Internal Revenue Service with $48 million. The largest recipients of program integrity contracts in fiscal 2019 were Qlarant Inc. ($58 million) and AdvanceMed, a subsidiary of NCI Inc. ($48 million), both of which hold active CMS contracts. Federal agencies have spent $917 million on artificial intelligence contracts in fiscal 2019 to date.

Vendors have until Nov. 20 to respond to the RFI.

Chris Cornillie is a federal market analyst with Bloomberg Government.

To contact the analyst on this story: Chris Cornillie in Washington at

To contact the editors responsible for this story: Daniel Snyder at; Jodie Morris at

acts. Federal agencies have spent $917 million on artificial intelligence contracts in fiscal 2019 to date.