How Payers are Using AI to Deny Claims and Dent Provider Revenue

As healthcare reimbursement evolves, hospitals are facing a new challenge: payers are increasingly using artificial intelligence (AI) to manage claims. Many providers may not realize AI tools are being used to review their claims, and these systems are not built with provider interests in mind. While AI has the potential to streamline processes, its current use in the revenue cycle is resulting in more claim denials, payment delays, and a greater need for appeals, particularly because payers often use AI to retroactively review medical necessity determinations. To navigate this AI-driven landscape, hospitals need to develop expertise to combat the biases and errors inherent in these systems.

Lack of transparency

One of the biggest issues with AI in claims processing is the lack of transparency. Payers rarely disclose that AI is being used or explain how it operates, and providers are often unaware of the algorithms driving these AI systems. This leaves hospitals with little information to contest AI-generated denials.

Without insight into the logic behind these denials, hospitals are at a disadvantage, especially given the added administrative burden of contesting them. For example, AI audits frequently occur after hospitals have completed due diligence, received authorization, and have been paid for a claim. AI systems may retroactively re-evaluate the claim and decide that medical necessity wasn’t met. This can lead to payment reversals, requiring hospitals to use even more resources to contest claims that were initially approved. In short, AI-driven post-payment audits delay payments and erode trust between hospitals and payers, putting hospitals under financial strain.

Time is critical

Once a claim is denied, hospitals are on the clock to appeal. Appeals require substantial resources and a clear understanding of why the claim was denied. 

Consider a diagnostic procedure that does not initially require approval but becomes a surgical procedure when a doctor discovers a tumor or lesion. AI might automatically reject that claim due to the lack of preauthorization, even though the situation evolved naturally and any physician would have acted in the same way. Without catching these AI-driven denials early, hospitals can lose significant revenue.

Similarly, AI algorithms may deny chemotherapy or radiation treatments if they continue beyond the approved period, even when a physician says the treatment must continue. Without timely reauthorization, hospitals risk substantial financial losses.

AI vs. AI: A losing battle?

In an effort to combat payer AI denials, some hospitals have implemented their own AI tools to handle claims. While this might seem like a good solution, it can backfire. Payers’ AI systems are increasingly sophisticated and can sometimes detect when they are countered by another AI system rather than a skilled human. This can trigger more denials, as payer systems may overlook or reject automated responses, perceiving them as less credible.

AI lacks the ability to interpret the complexities of medical care in the same way a trained clinician can. When AI systems battle each other, the result is often a cascade of errors and missed opportunities for appeal. Hospitals that rely too heavily on AI without human oversight may find themselves stuck in a cycle of denials that is difficult to escape. Payer AI, recognizing the absence of human expertise, may become even more aggressive in issuing denials.

Tackling the AI challenge

Despite the difficulties AI presents, hospitals can take several steps to reduce its impact on revenue:

1. Leverage human expertise: AI errors often require human intervention. Clinicians and revenue cycle teams trained to anticipate AI-related denials, combined with thorough documentation and context, can reduce denials and improve success rates on appeals.
2. Understand the algorithms: Hospitals must develop an understanding of how AI systems work. Careful analysis of medical charts, clear communication with doctors, and identification of the root causes of denials can prevent future issues before they arise.
3. Adapt to new systems: In some cases, hospitals have successfully reduced denials by adapting to new scoring systems introduced by payer AI algorithms. For example, one hospital significantly reduced sepsis-related claim denials after understanding and adjusting to a new scoring system used by a payer’s AI. This proactive approach saved the hospital thousands of dollars per care episode.
4. Recognize patterns and stay proactive: Hospitals should identify patterns in denials and adjust processes accordingly. Proactively securing reauthorizations for treatments like chemotherapy, which often have limited approval periods, can prevent revenue losses due to lapses in authorization.

Looking ahead

As AI continues to play a larger role in claims processing, hospitals will face growing challenges related to denials, audits, and appeals. However, these challenges also present an opportunity to improve revenue cycle management by balancing human expertise with technology. Understanding how payer AI operates and ensuring human oversight in the claims process can help hospitals reduce erroneous denials.

While AI can assist, human judgment remains essential in managing complex medical claims. Hospitals should avoid overreliance on AI tools to fight denials. By combining clinical expertise with a strategic approach to addressing payer AI-driven decisions, hospitals can better protect their revenue and avoid the costly consequences of increased denials.

Ultimately, hospitals that maintain a strong human element in their revenue cycle processes will be better positioned to navigate the challenges of AI-driven claims denials and minimize their impact on financial performance.

Image: tumsasedgars, Getty Images

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