CMS is launching the Wasteful and Inappropriate Services Reduction (WISer) Model, a six-year pilot program that will begin on January 1, 2026. The goal of the program will be to test whether advanced technologies like Artificial intelligence (AI) and Machine Learning (ML) can help CMS identify and reduce unnecessary spending, streamlining prior authorization processes for  Medicare fee-for-service beneficiaries. The model will leverage companies with expertise in managing the prior authorization process for other payers using enhanced technology. The model doesn’t change Medicare coverage and doesn’t apply to people with Medicare Advantage (MA). It will operate across six states (New Jersey, Ohio, Oklahoma, Texas, Arizona and Washington), affecting nearly one in five Medicare FFS beneficiaries. 

Meet WISeR 

The WISeR model is being implemented to curb waste and inefficiency in Medicare, where roughly 25% of health care spending goes toward services that according to CMS offer little or no clinical benefit. The model participants will be third-party technology vendors that apply to participate in the model and have prior experience managing the prior authorization processes for other payers. Applications must be submitted by July 25, 2025. While the vendors will volunteer to participate, health providers in the selected states will be subject to the model’s requirements, regardless of willingness to participate. The initial list of services subject to prior authorization includes deep brain stimulation for Parkinson’s disease, epidural steroid injection for pain management, cervical fusion, skin and tissue substitutes, and knee arthroscopy for osteoarthritis. The model excludes inpatient-only services, emergency services, and services that, if substantially delayed by a prior authorization, would pose a risk to patients.  Click here for a summary of the WISer Model. 

Why It Matters 

The adoption of this model signals CMS’ intention to increase the use of prior authorization in traditional Medicare, despite some of the challenges being experienced in the MA space. CMS is also testing how technology could be used to improve prior authorization processes, furthering the Administration’s goals to integrate AI in government programs. The features adopted in this model, such as exempting providers that achieve a provisional affirmation rate of 90% or higher during a performance period (AKA gold carding), could serve as a blueprint for commercial insurance and state legislatures trying to pass their own prior authorization bills.   

AHPA extends our gratitude to emerging colleagues Aimal Irteza and Jenna Wilson,
guest co-authors of this article. Aimal is an undergraduate studying Political Science and Public Policy at Rollins College. Jenna is completing her graduate studies at the University of Central Florida’s School of Global Health Management and Informatics.