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As Medicare grapples with fraud and waste, prior authorization aims to protect the system but often delays critical treatments. Can technology offer a smarter solution?
For millions of Americans, prior authorization has become a dreaded term in healthcare. It represents the bureaucratic hurdles patients and providers must clear before accessing essential medical treatments. This process, which began as a tool to manage costs and ensure safe care, now often stands as a significant barrier to timely and effective treatment. However, recent innovations suggest that there might be a smarter path forward for Medicare.
The impact of prior authorization on patient care is profound. During my 19 years as an interventional radiologist at Mayo Clinic, I witnessed the frustration firsthand. Each Monday, I would find a stack of manila folders filled with denied prior authorizations waiting for me. My requests for necessary treatments were frequently blocked, forcing my nurse to spend half her time on paperwork instead of providing hands-on care.
I was not alone in this struggle. According to the American Medical Association's (AMA) 2024 Prior Authorization Physician Survey, a staggering 93% of physicians reported that prior authorization has caused delays in patient care. Even more alarmingly, over a quarter (29%) said that prior authorization had led to serious adverse events for their patients.
Prior authorization was introduced by insurers in the 1960s with the goal of reducing in-patient medical costs while promoting safe and efficient care. Insurers argue that it is a crucial tool in combating fraud, waste, and abuse (FWA) in healthcare. However, what began as a well-intentioned measure has evolved into a cumbersome process that often hinders rather than helps.
The problem of FWA in Medicare is significant. Estimates suggest that up to 42% of Medicare beneficiaries annually receive care that is essentially worthless. This systemic issue not only wastes resources but also undermines the trust patients have in their healthcare system. Both physicians and payers are united in their concern over FWA, recognizing it as a major challenge that must be addressed.

To strike a balance between ensuring necessary care and combating FWA, new innovative approaches are emerging. One promising solution is the use of artificial intelligence (AI) in prior authorization processes. While AI has primarily been employed in the commercial healthcare sector, it is now making its way into Medicare through the WISeR (Waste, Inefficiency, and Systemic Errors Reduction) model.
The WISeR model aims to reframe prior authorization as a resource rather than a roadblock. By leveraging AI, the process can become more efficient and accurate. For example, AI algorithms can quickly analyze large datasets to identify patterns of FWA, allowing for targeted interventions that do not unduly burden patients or providers.
The goal is to reduce administrative burdens and redesign workflows around human needs. This approach creates space for what matters most: the connection between clinicians and patients. By automating routine tasks, healthcare professionals can focus more on patient care and less on paperwork.
Michael Blackman, MD, MBA, Chief Medical Officer of Greenway Health®, highlights this shift in his recent article, "Inside an Automated Healthcare Practice: Redesigning Care Around People, Not Paperwork." He emphasizes that by streamlining administrative processes, healthcare providers can devote more time to building meaningful relationships with their patients.
Prior authorization has become a contentious issue in healthcare, but it is not without its merits. By embracing innovative technologies like AI and models like WISeR, Medicare can find a balanced approach that ensures necessary care while effectively addressing the pervasive problem of FWA. The ultimate goal should be to create a system that puts patients first, providing timely, effective, and trustworthy care.
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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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30 April 2026
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