Earlier this year the Centers for Medicare and Medicaid Services introduced the “Wasteful and Inappropriate Service Reduction” model, a series of prior authorization requirements designed to ensure timely and appropriate Medicare payment for select items and services in six states (New Jersey, Ohio, Oklahoma, Texas, Arizona, and Washington) that take effect Jan. 1.
As part of the “WISeR” requirements, CMS selected tech vendors to implement enhanced technological models to scrutinize prior authorizations involving Medicare recipients. The program set out to save money by targeting “a specific subset of items and services that may have little to no clinical benefit for certain patients and that historically have had a higher risk of waste, fraud and abuse.”
Quickly, objections were raised. On Nov. 7, a group of six representatives introduced new legislation that would roll back the WISeR model, claiming their bill will “protect seniors from new red tape.” In a press release explaining his opposition to the WISeR model, Rep. Ami Bera, M.D. writes that decisions involving patients’ health “should be made by doctors, not by algorithms designed to cut costs.”
Dr. Bera is not alone in framing the WISeR debate around the use of technology (such as artificial intelligence or machine learning tools) in Utilization Management decisions (such as prior authorization). Yet there is danger in extrapolating the particulars of one legislative effort to every AI or ML use case being developed and deployed within the healthcare industry.
ML and other advanced analytics should be used in Medicare — but only to support, never replace, human clinical judgment. ML-enabled utilization management (whether prior or concurrent authorization) should be employed with two objectives:
- Improving patient outcomes and access to medically necessary care
- Reducing fraud, waste and abuse in a transparent, accountable way
ML applied to utilization management, especially in Original Medicare, should be designed to:
- Target clearly defined areas of low-value or potentially harmful care
- Maintain and strengthen beneficiary protections and appeal rights
- Reduce administrative burden on providers and suppliers
- Adhere to recognized AI risk frameworks and industry standards, such as the NIST AI Risk Management Framework (AI RMF 1.0).
The healthcare industry — payers and providers — are monitoring the current policy debate. CMS has stated that WISeR will use enhanced technologies, including ML, to streamline review of a narrow set of services associated with fraud, waste and potential patient harm, while requiring human clinician review for non-affirmations and preserving appeal rights.
Policymakers, provider groups, and advocates have raised concerns that expanded prior authorization, especially when linked to private vendors and AI tools, could increase red tape, delay needed care, and replicate problematic patterns seen in Medicare Advantage, where many denials are overturned on appeal and physicians report substantial administrative burden and adverse events linked to prior authorization.
Any balanced, evidence-driven approach must rest on the following pillars:
- Proceed with carefully scoped, transparent pilots of ML-enabled utilization management in Original Medicare, focused on clearly documented low-value services
- Embed the safeguards described above (“human in the loop” review, strong appeals, robust transparency, and ongoing fairness monitoring) into the model design pre-deployment
- Commit to modifying or discontinuing any model if evaluations show unacceptable impacts on access to medically necessary care, health equity, or provider participation, even if nominal program savings are achieved
The WISeR model includes among its goals: “increasing transparency on existing Medicare coverage policy.” But its commitment to run for six performance years, from January 1, 2026 to December 31, 2031, must include room for recourse if the ends (cutting costs) cannot justify the means.
About Gregg Killoren
Gregg Killoren is General Counsel at Xsolis specializing in regulatory strategy, corporate compliance, and legal risk management across healthcare and technology. He brings deep expertise in navigating complex legal frameworks and supporting high-growth, mission-driven organizations.
