Understanding Prior Authorization: A System Under Scrutiny
For countless Americans, navigating the healthcare system often involves a complex and frequently frustrating hurdle known as prior authorization. This administrative process requires healthcare providers to obtain approval from insurance companies before a service, medication, or procedure can be rendered. While originally intended as a mechanism to control costs, prevent overuse of services, and ensure patients receive medically appropriate care, prior authorization has evolved into a formidable barrier for many. Personal accounts abound, detailing the tribulations patients face as they jump through bureaucratic hoops to secure approval for physician-recommended treatments, ranging from essential prescription medications to critical surgical procedures.
The American Medical Association (AMA) and other healthcare organizations have long voiced concerns that prior authorization often leads to significant care delays, causing patients to abandon recommended treatments altogether. A 2025 AMA survey of physicians highlighted these concerns, revealing that a large majority of doctors believe the process significantly hinders timely patient care. Such delays can have severe consequences, potentially leading to worsening health conditions or reduced treatment efficacy, particularly for patients with progressive or time-sensitive illnesses.
The Allure of Artificial Intelligence in Healthcare Approvals
Given the immense volume of prior authorization requests and the administrative burden they place on both providers and insurers, the promise of artificial intelligence offers an enticing solution. AI, with its capacity to rapidly process and analyze vast datasets, could theoretically expedite the approval of unambiguously allowable claims. By automating the review of straightforward cases that clearly meet coverage criteria, AI could potentially reduce human workload, speed up decision-making, and allow healthcare professionals to focus on more complex cases requiring clinical judgment. Proponents argue that this efficiency gain could lead to faster access to care for patients and a reduction in the administrative costs currently associated with manual reviews.
The concept is simple: feed AI algorithms historical data, medical guidelines, and policy rules, allowing it to quickly identify patterns and make preliminary determinations. This could, in theory, free up human reviewers to focus on ambiguous or exceptional cases, thereby optimizing the entire process. Such an application aligns with broader trends of digital transformation in healthcare, aiming to enhance operational efficiency and improve service delivery.
Growing Pains: Physician and Patient Concerns with AI Integration
Despite the potential for increased efficiency, the integration of AI into prior authorization is facing considerable resistance and scrutiny. A primary concern revolves around the potential for AI-driven systems to increase wrongful denials of health insurance coverage. Critics fear that without adequate human oversight and transparent algorithms, AI might err on the side of denial, especially in complex cases that require nuanced clinical understanding.
The 2025 American Medical Association survey of physicians underscored these anxieties, with a significant 61 percent of doctors expressing worry that AI would exacerbate denials of treatments they deem medically necessary. This concern is not unfounded; if AI models are primarily optimized for cost-cutting, they might be programmed to identify reasons for denial more readily than for approval, particularly for expensive or novel treatments.
Health policy analyst Camm Epstein articulated this sentiment in an email to Undark, stating that “AI should be used to make appropriate care easier to approve, not necessary care easier to deny.” This statement encapsulates the core ethical dilemma: how to leverage AI’s power for efficiency without compromising patient access to vital care. The AMA advocates for robust safeguards, including requiring insurers to provide detailed clinical reasoning to justify denials and demanding greater transparency regarding the AI algorithms used in decision-making.
The WISeR Initiative: A Deep Dive into the Government’s Pilot
In response to the persistent challenges of waste and fraud within the healthcare system, the Centers for Medicare and Medicaid Services (CMS) launched the Wasteful and Inappropriate Service Reduction (WISeR) Model this year. This demonstration project, which runs through December 2031, is currently being piloted in six states and represents a significant expansion of AI’s role in Original Medicare. The primary objective of WISeR is to identify and decrease unnecessary procedures and spending by combining advanced technologies, such as machine learning, with human clinical review.
The WISeR model specifically targets services that CMS believes are vulnerable to overuse, fraud, and abuse. These include, but are not limited to, skin and tissue substitutes, electrical nerve stimulator implants, and knee arthroscopy for knee osteoarthritis. By applying AI to these areas, CMS aims to "ensure timely and appropriate Medicare payment for select items and services," as stated on its official website.
The expansion of prior authorization, particularly with AI, into Original Medicare is a notable shift. Historically, prior authorization has been less prevalent in Original Medicare compared to Medicare Advantage plans. This shift has ignited a debate about whether such a move will truly benefit patients or merely introduce the same hurdles that have plagued the privately run Medicare Advantage system.
Echoes from Medicare Advantage: A Precedent for Caution
The experience within Medicare Advantage (MA) offers a critical lens through which to view the potential impacts of AI in Original Medicare. Medicare Advantage, which now enrolls roughly 55 percent of Medicare-eligible seniors and disabled individuals, has extensively utilized prior authorization. Data from MA plans reveal that insurers issue millions of full or partial claim denials annually based on prior authorization.
Federal government reports from the Office of Inspector General (OIG) have raised significant red flags. A 2022 OIG memorandum highlighted that in more than one in ten instances, Medicare Advantage plans denied beneficiaries’ access to services even when they appeared to meet coverage rules. While a substantial number of these denials (81% in 2024, according to KFF data) are overturned upon appeal, the initial denial still creates delays, stress, and potential harm for patients. Furthermore, OIG reports in June indicated that some MA plans even rejected requests for skilled nursing and rehabilitation admissions, erecting obstacles to medically appropriate care.
These statistics from Medicare Advantage underscore the public’s perception of prior authorization as a "major burden," as indicated by a KFF public opinion poll. Patients caught in "prior authorization purgatory," as reported by NBC News, often run out of time or treatment options while awaiting approval, sometimes with dire consequences. This history provides a stark warning for the implementation of AI-driven prior authorization in Original Medicare, suggesting that efficiency gains could come at the cost of patient access and well-being if not meticulously managed.
A newly released Commonwealth Fund survey in June 2026 further illustrated the pervasive impact of coverage denials. Roughly one in five American working-age adults with private insurance reported that they or a family member were denied coverage for physician-recommended medical care in 2025. Among those who experienced a prior authorization denial, 41 percent reported delayed care, and over a quarter stated that their health problem worsened as a result. These findings paint a grim picture of the current prior authorization landscape and amplify concerns about AI’s potential to exacerbate these issues.
Administrative Burden and the Profit Motive
A significant point of contention surrounding the WISeR model, and AI in prior authorization generally, is the financial incentive structure. Vendors participating in the WISeR model are reportedly hired to carry out AI-driven prior authorization and earn a share of what CMS terms "averted expenditures." This arrangement, critics argue, creates a direct financial incentive for these vendors to reject care requests, potentially prioritizing cost savings over patient needs.
Wendell Potter, a prominent advocate for health insurance reform and a former executive at Cigna, has extensively covered the political pushback against the WISeR model on his Substack publication "HEALTH CARE un-covered." He, along with Zena Wolf, a researcher with the Center for Health & Democracy, cited investigations from reputable news outlets like the Washington Post, KFF Health News, and the Seattle Times, suggesting that in its initial months, the WISeR model has already caused care delays and denials in each of the six pilot states.
Beyond denials, the introduction of automated processes does not necessarily eliminate the administrative burden on healthcare providers. Instead, it can shift the burden, requiring additional time and resources to deal with AI-generated denials, submit appeals, and communicate with patients about complex coverage issues. This "administrative arms race," as described by physician Jared Dashevsky, where systems compete to deny faster and appeal faster, further strains healthcare resources and detracts from direct patient care.
These concerns are not confined to advocacy groups. Several lawmakers have introduced resolutions and amendments to block funding for the WISeR model, citing threats to patient access and raising broader questions about profit-making from discouraging medically necessary care. The debate highlights the delicate balance between financial stewardship of public funds and ensuring equitable access to healthcare services.
Policy Responses and Industry Pledges
The challenges posed by prior authorization have not gone unnoticed by policymakers and industry stakeholders. Both government administrations and private insurers have attempted to implement reforms to alleviate the burden.
Under the Biden administration, a rule issued in 2024 aimed to streamline prior authorization for patients with government-run plans and for physicians. This rule mandated that insurers make certain prior authorization decisions within 72 hours for urgent requests and seven calendar days for non-urgent requests. These crucial timeline requirements officially went into effect on January 1 of this year for most public sector health plans, signaling a commitment to reducing delays.
Interestingly, the Trump administration appears to be navigating a bifurcated approach to prior authorization. While expanding its use with AI in Original Medicare through WISeR, the administration has simultaneously pressed private insurers, including Medicare Advantage plans, to lessen and streamline their prior authorization processes. CMS Administrator Mehmet Oz publicly warned insurance company executives that if they did not ease the burden themselves, the federal government would impose regulations.
In response to this pressure and to potentially preempt further executive action or legislative intervention, health plans have released data suggesting compliance with administration demands. An industry-based survey revealed an 11 percent decline in prior authorization requests between June 2025 and April 2026. However, it remains unknown whether this reduction in requests has translated into a decrease in the overall denial rate, a crucial metric for patient access.
Furthermore, in a survey conducted last year, all responding health plans agreed with the statement that "AI or algorithms without clinician or practitioner review are not used to deny prior authorization requests that involve medical necessity or clinical considerations." Insurers also pledged greater transparency regarding the clinical reasoning underpinning prior authorization decisions. These commitments aim to alleviate concerns about a lack of human oversight in AI-driven decisions, though skepticism among detractors remains high.
The Path Forward: Navigating Innovation and Patient Safeguards
The introduction of AI into healthcare’s prior authorization landscape represents a critical juncture. While the technology holds undeniable promise for enhancing efficiency and reducing administrative waste, its implementation carries significant risks, particularly concerning patient access to care and the potential for algorithmic bias leading to wrongful denials.
Jared Dashevsky, a physician and founder of Healthcare Huddle, succinctly captured the prevailing sentiment among many healthcare professionals: AI could "eliminate barriers, reduce administrative waste, give us more time with patients." However, he cautions that "that’s not what’s being built." Instead, he observes an "arms race to deny faster and appeal faster. More automation of a broken system that shouldn’t exist in its current form."
The future of AI in prior authorization will depend heavily on the ability of policymakers, healthcare providers, and technology developers to create systems that prioritize patient well-being and clinical necessity over pure cost-saving metrics. This necessitates robust regulatory frameworks, transparent algorithms, and meaningful human oversight at every stage of the decision-making process. Without these safeguards, the promise of AI to fix a tortuous system risks making it even more opaque, burdensome, and detrimental to patient health. The ongoing pilot of the WISeR model will be a crucial test case, providing valuable insights into the real-world implications of AI-driven insurance decisions and shaping the broader trajectory of artificial intelligence in healthcare.
