For millions of Americans, navigating the healthcare system often involves a frustrating and complex hurdle known as prior authorization. This administrative process, which requires healthcare providers to obtain approval from an insurance company before a prescribed medication, medical procedure, or service is covered, has long been a source of contention. While designed to prevent unnecessary care and control costs, prior authorization frequently leads to significant delays in treatment, patient distress, and an overwhelming administrative burden on medical professionals. Now, the introduction of artificial intelligence (AI) into this already contentious process promises both a potential solution and a new layer of complexity, with the federal government actively exploring its use in Medicare.
The Enduring Burden of Prior Authorization
Prior authorization, or "prior auth," is ostensibly a mechanism to ensure that healthcare services are medically necessary and cost-effective, steering patients towards less expensive or more appropriate alternatives when available. Its proponents argue that it acts as a crucial check on overutilization and unwarranted spending, thereby helping to keep healthcare costs manageable for insurers and, theoretically, for consumers. However, the practical application of prior authorization often falls short of these ideals, creating significant barriers to timely and essential medical care.
Personal accounts of patients struggling through bureaucratic hoops to secure approval for life-saving drugs or critical surgeries are widespread and deeply concerning. These stories frequently highlight the human cost of administrative delays, where patients find themselves in a precarious "purgatory" as they await insurer decisions, sometimes running out of time or viable treatment options. Physicians overwhelmingly express concerns about the detrimental impact of prior authorization on patient care. A large majority of doctors, according to a 2025 American Medical Association (AMA) survey, report that prior authorization causes care delays, which can lead patients to abandon recommended treatments altogether. This can result in worsening health conditions, increased suffering, and potentially more severe medical interventions down the line.
The administrative burden on medical practices is also substantial. Doctors and their staff spend countless hours completing paperwork, making phone calls, and appealing denials, diverting valuable resources away from direct patient care. The AMA has long advocated for comprehensive reforms, including requiring insurers to provide detailed clinical reasoning for denials and greater transparency regarding the algorithms and criteria used in their decision-making processes. Health policy analyst Camm Epstein encapsulated this sentiment, writing, "AI should be used to make appropriate care easier to approve, not necessary care easier to deny."
The Promise and Peril of AI in Healthcare Approvals
In theory, artificial intelligence, with its unparalleled capacity to process and analyze vast datasets, offers an enticing solution to the inefficiencies plaguing prior authorization. AI algorithms could rapidly sort through medical records, clinical guidelines, and insurance policies to identify unambiguously allowable claims, thereby potentially expediting approvals and significantly reducing care delays. This could free up human staff at both insurance companies and healthcare providers to focus on more complex cases, enhancing overall system efficiency.
However, the integration of AI into such a critical decision-making process is not without significant trepidation. Critics and medical professionals alike voice profound concerns that AI could exacerbate, rather than alleviate, the problem of wrongful denials. The same 2025 AMA survey revealed that 61 percent of physicians feared that AI tools would lead to an increase in denials for treatments they deemed medically necessary. The concern centers on the potential for AI to be programmed with criteria that prioritize cost-saving over patient well-being, or to misinterpret complex medical nuances that a human clinician would understand. Without robust oversight and transparency, AI-driven prior authorization could become an "arms race to deny faster and appeal faster," as physician and media platform founder Jared Dashevsky put it, automating a broken system rather than fixing it.
The WISeR Model: AI’s Expansion into Original Medicare
Despite these reservations, the federal government is actively exploring AI’s role in prior authorization. Under the Trump administration, the Centers for Medicare and Medicaid Services (CMS) launched a demonstration project this year called the Wasteful and Inappropriate Service Reduction (WISeR) Model. This initiative, scheduled to run through December 2031 in six pilot states, aims to leverage AI, specifically machine learning, combined with human clinical review, to identify and reduce waste, fraud, and abuse in Original Medicare.
The WISeR model targets specific services believed to be vulnerable to overuse, such as skin and tissue substitutes, electrical nerve stimulator implants, and knee arthroscopy for osteoarthritis. While prior authorization has been extensively used in Medicare Advantage plans, its deployment in Original Medicare, a fee-for-service program, is relatively rare and represents a significant shift in policy. CMS states that WISeR is designed to "ensure timely and appropriate Medicare payment for select items and services." However, the model has immediately drawn criticism from patient advocates and healthcare reformists.
A primary concern revolves around the financial incentives embedded within the WISeR model. Vendors hired to implement the AI-driven prior authorization process are compensated with a share of "averted expenditures" — essentially, a portion of the money saved by rejecting care requests. This payment structure raises serious ethical questions about whether the system incentivizes denials, potentially compromising patient access to medically necessary care for financial gain. Critics, including Wendell Potter, a former health insurance executive turned advocate for health insurance reform, have highlighted the political pushback against WISeR, noting early reports from publications like the Washington Post, KFF Health News, and the Seattle Times that suggest the model has already caused care delays and denials in the pilot states during its initial months. Lawmakers have also responded, with several introducing resolutions and amendments to block funding for WISeR, citing the threats it poses to patient access.
Lessons from Medicare Advantage
The existing landscape of prior authorization in Medicare Advantage (MA) plans offers a stark preview of the challenges AI could bring to Original Medicare. Medicare Advantage, the privately run alternative to traditional Medicare, now enrolls approximately 55 percent of Medicare-eligible seniors and disabled individuals. In 2024 alone, MA insurers issued millions of full or partial claim denials based on prior authorization.
Federal government reports from the Department of Health and Human Services (HHS) Office of Inspector General (OIG) have repeatedly flagged significant issues within MA. A 2022 OIG memorandum, for instance, revealed that Medicare Advantage plans denied beneficiaries access to services in more than one in ten instances, even when those services apparently met Medicare coverage rules. Subsequent reports in 2026 documented instances where the largest MA organizations denied requests for long-term acute care and inpatient rehabilitation at alarmingly high rates. These findings underscore a long-standing concern: that profit-making incentives in private insurance can lead to erecting obstacles to medically appropriate care.
While it is true that MA plans often overturn a significant percentage of denials upon appeal (81 percent in 2024, according to KFF), the appeals process itself is often complicated, cumbersome, and time-consuming for patients and providers. This administrative burden can deter patients from pursuing necessary care or result in critical delays that worsen their health outcomes. The public consistently ranks prior authorization as a major burden, a sentiment reinforced by a newly released Commonwealth Fund survey in June 2026. This survey found that roughly one in five American working-age adults with private insurance reported a denial of coverage for physician-recommended medical care in 2025. Of those denied, 41 percent experienced care delays, and more than a quarter reported that their health problem worsened as a direct result.
Regulatory Responses and Industry Commitments
Recognizing the pervasive issues with prior authorization, both government administrations and the insurance industry have attempted to implement reforms. In 2024, the Biden administration issued a rule designed to streamline the prior authorization process and reduce delays for patients enrolled in government-run health plans. This rule mandated that insurers make prior authorization decisions within 72 hours for urgent requests and seven calendar days for non-urgent requests. These timeline requirements officially took effect on January 1 of this year for most public sector health plans.
Paradoxically, while CMS expands the use of AI-driven prior authorization in Original Medicare through WISeR, the Trump administration has also pressured private insurers, including Medicare Advantage plans, to lessen and streamline their prior authorization protocols. CMS Administrator Mehmet Oz has publicly warned insurance executives that if they do not ease the burden of prior authorization themselves, the federal government would impose further regulations.
In response to this pressure and possibly to preempt further executive or legislative action, health plans have released data suggesting compliance. An industry-based survey revealed an 11 percent decline in requests for prior authorization between June 2025 and April 2026. Insurers have also pledged to standardize electronic requests by 2027 and to "reduce the volume of medical services subject to prior authorization" by 2026, targeting common procedures like colonoscopies and cataract surgeries. Furthermore, an industry group survey conducted last year indicated that all responding health plans agreed with the statement: "AI or algorithms without clinician or practitioner review are not used to deny prior authorization requests that involve medical necessity or clinical considerations." Insurers have also promised increased transparency regarding the clinical reasoning behind their prior authorization decisions.
While these commitments may alleviate some concerns about the lack of human oversight in AI-driven decisions, the ultimate impact on denial rates remains unknown. The effectiveness of these pledges in truly transforming the prior authorization landscape and improving patient access to care is yet to be definitively demonstrated.
A Call for Principled AI Deployment
The divergent approaches and contradictory signals from the government, combined with the industry’s self-regulatory efforts, paint a complex picture. The core tension remains between the potential for AI to drive efficiency and cost savings, and the imperative to ensure equitable and timely access to medically necessary care.
Experts and advocates emphasize that the successful and ethical integration of AI into prior authorization hinges on strict guidelines and oversight. Transparency in AI algorithms, a commitment to human clinical review for all denial decisions, and clear accountability mechanisms are crucial. The goal, as many argue, should be to deploy AI as a tool to support clinicians and patients, making it easier to approve appropriate care, rather than a mechanism to erect new barriers.
As the WISeR model progresses and AI becomes more embedded in healthcare administration, the debate over its role in prior authorization will undoubtedly intensify. The outcome will have profound implications for patient well-being, the practice of medicine, and the future of healthcare delivery in the United States. Without careful implementation, robust ethical frameworks, and an unwavering focus on patient-centered care, the promise of AI to fix the tortuous system of prior authorization risks instead making it worse.
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