How Centers for Medicare and Medicaid Services uses AI

In the WISeR model, the US Centers for Medicare & Medicaid Services works with technology companies that use AI and machine learning, together with clinical review, to review prior authorization requests and claims before payment for a selected set of Original Medicare services with a history of waste, fraud and abuse. It runs from January 1, 2026 to December 31, 2031 in New Jersey, Ohio, Oklahoma, Texas, Arizona and Washington. Every recommendation for non payment is decided by an appropriately licensed clinician. Six technology companies take part, one per state, and are paid a share of the averted spending, adjusted for performance measures that include provider experience. The model page reports no results yet.

The Centers for Medicare and Medicaid Services, in its Office of Financial Management, reported in the 2025 federal AI use case inventory a tool that systematically reviews new executive orders and compares them against CMS's existing business and technical requirements. It identifies where current processes might need adjustment and produces recommendations and a gap analysis, to support compliance so CMS operations align with federal mandates. The entry was at the pre deployment stage when reported, with no vendor or contracting approach yet disclosed.

CMS, an international law firm headquartered in Frankfurt with more than 7,000 lawyers across 50 plus countries, ran an initial pilot and then a twelve month pilot with 300 lawyers before rolling Harvey out firm wide. A customer story published by Harvey reports that by the summer of 2025, CMS had more than 3,000 lawyers using the assistant for tasks including document review, and that over 95% of them were active users.

A team at the US Centers for Medicare and Medicaid Services is piloting AI that analyses a high volume of complaint cases to identify root causes and trends, maps them to the applicable regulatory citations, draws a sample for subject matter experts to validate and recommends next steps. The stated aim is to reduce repeat issues that delay benefits or access to care and to improve health plan compliance. The 2024 inventory describes an earlier proof of concept that used a large language model on complaint data used by the same office (OPOLE). No results are published.

The US Centers for Medicare and Medicaid Services runs a machine learning system, in production since October 2024, that reads the hundreds of consumer complaints its Federally facilitated Exchange call center receives every day and categorizes each one into one or more categories under CMS's Complaint Review Standard Operating Procedure. The categorization tells CMS the nature of the complaint, the action needed and which CMS component should handle it, work previously done by the Exchange Complaints Review Contractor reading and coding each complaint by hand. No processing time figure is disclosed for the automated system; the manual baseline is up to 10 minutes per complaint including quality review.

The CMS Division of Payment Reconciliation monitors Prescription Drug Event (PDE) data from Medicare Part D for accuracy. It reported an AI project to identify outliers in that data so that errors can be corrected through outreach to the plans and improper payments, an overpayment or an underpayment of a government benefit, are prevented. The project was at the initiated stage in the 2024 federal inventory; no results are published.

The Centers for Medicare and Medicaid Services (CMS) built the Fraud Prevention System (FPS), a data analytic system implemented in 2011 that develops leads for fraud investigations by CMS program integrity contractors and denies improper Medicare fee for service claims before payment through automated, rule based prepayment edits.

The US Government Accountability Office (GAO) reviewed CMS's use of FPS and reported that, in fiscal year 2016, FPS backed about a fifth of Medicare fraud investigations, contributed to provider payment suspensions during ongoing investigations, and its prepayment edits denied claims that violate documented Medicare rules or policies.

GAO states that these edits "do not analyze individual claims to automatically deny them based on risk alone or the likelihood that they are fraudulent," and that CMS does not have the authority to use FPS to deny a claim automatically based on risk alone. CMS also helped establish the Healthcare Fraud Prevention Partnership, which pools claims data across public and private payers to spot billing patterns no single payer's data would show.