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As concerns mount over potential denials and health disparities, a group of Democratic lawmakers is pushing to scrap the WISeR model through the Congressional Review Act.
In January 2026, the Centers for Medicare & Medicaid Services (CMS) launched the Wasteful and Inappropriate Service Reduction (WISeR) model, introducing prior authorization into traditional Medicare for certain services in selected states. The program, which leverages artificial intelligence (AI), aims to reduce unnecessary, duplicative, and low-value care. However, this ambitious initiative is now facing a significant challenge as Congressional Democrats are leading an effort to repeal it.
The WISeR model is currently being tested in Arizona, New Jersey, Ohio, Oklahoma, Texas, and Washington, with the support of tech vendors such as Cohere Health, Genzeon Corporation, Humata Health, Innovaccer, Virtix Health, and Zyter. The program's use of AI to streamline prior authorization processes has been a double-edged sword, according to Neil Patil, senior fellow and policy director at Georgetown’s Center on Health Insurance Reform.
While prior authorization can help reduce wasteful care, it also poses significant risks. "Prior authorization can lead to denials and delays of necessary care, increasing out-of-pocket expenses for beneficiaries," Patil explained in an interview. The integration of AI into the process adds another layer of complexity. Concerns have been raised about AI algorithms potentially denying care based on flawed or biased data, which could exacerbate existing health disparities.
On May 20, a group of Democratic lawmakers introduced a resolution to repeal the WISeR model through the Congressional Review Act (CRA). The CRA gives Congress the power to review and overturn federal agency rules. Leading this effort are Sen. Ron Wyden (D-Oregon), Rep. Suzan Delbene (D-Washington), and Rep. Greg Landsman (D-Ohio).
The push for repeal came after a May 12 report from the Government Accountability Office (GAO) found that the WISeR model imposes new prior authorization requirements on some Traditional Medicare providers, making it subject to congressional review under the CRA. This finding has bolstered the Democrats' case against the program.

"The use of artificial intelligence and algorithms in applying prior authorization has raised serious concerns," said Sen. Wyden. "There are reports of inappropriate denials of care based on flawed algorithms, which can disproportionately affect vulnerable populations."
The WISeR model's reliance on AI has also drawn scrutiny from healthcare providers and patient advocates. A recent article in Fierce Healthcare highlighted the program's potential to snarl patients and doctors with errors and delays. The combination of administrative burden and technology-based decision-making has created a challenging environment for both patients and healthcare professionals.
The fate of the WISeR model now rests in the hands of Congress. If the resolution passes, it would effectively repeal the program, preventing CMS from implementing or continuing it. However, the outcome is far from certain, as the resolution must navigate through both chambers of Congress and potentially face a presidential veto.
For now, beneficiaries in the six states where the WISeR model is being tested will continue to experience its impacts. The coming months will be crucial for determining whether this ambitious attempt to reduce wasteful care using AI will move forward or be halted by legislative action.
As the debate continues, it's clear that the use of AI in healthcare policy remains a contentious issue, with significant implications for patient access, provider burden, and health equity. The outcome of this repeal effort could set important precedents for future regulatory actions involving technology in healthcare.
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Original Sources
Is It Likely That the WISeR Model Will be Repealed? - MedCity News
↗ https://medcitynews.com/2026/06/is-it-likely-that-the-wiser-model-will-be-repealed
About the author
Amara's entry point into AI was an epidemiology role at a London research hospital, where she spent five years studying how digital health tools reached — or conspicuously failed to reach — underserved communities. Watching early algorithmic systems in healthcare quietly entrench existing inequalities, she redirected her career toward the systemic consequences of AI at scale. She covers AI through an unflinching lens: who benefits, who bears the cost, and what evidence actually says versus what the press release claims. Her writing is calm and precise, but she doesn't mistake balance for neutrality.
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