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.

No outcome disclosed.

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.

No outcome disclosed.

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.

No outcome disclosed.