Harnessing AI and Automation to Combat Payer Denials

In the intricate landscape of healthcare, payer denials pose a significant challenge for providers. In recent years, denial rates have increased partly due to payers’ use of artificial intelligence (AI). With the click of a button, dozens of claims can be denied not from a lengthy medical review process, but from insurance plans incorporating their own set of robotics and AI algorithms.  

These denials are often the result of errors in coding, documentation or authorization, and can lead to substantial lost reimbursement for care already provided. In addition to the lost revenue, CMS estimates these denials add more than $7.2 billion in administrative costs annually that providers spend on the appeals process.   

Michele Forgues Lackie, the chief financial officer at UW Medicine’s Valley Medical Center, recently talked to R1 about this pervasive issue. “Denials are at an all-time high. There’s no question,” she said. She believes this is happening because many providers lack the necessary automation resources to fight the payer’s AI technology. 

Similarly, Jim Wilson, the chief financial officer at Mayo Clinic Health System agrees that denials – and payer-provider relationships in general – seem to be getting harder. He recently told R1, “The best way to win a payer-provider disagreement is with facts and the use of data-driven analytics.” He says that using technology and data to back-up a provider’s position regarding medical treatment is key to receiving the right payment and can help clarify the appropriate course of action in a particular situation and potentially expedite the resolution of denials.  

Defying denials early

The best way to fight denials is to avoid them in the first place. The multi-faceted approach to claims management starts with negotiating solid payer contracts and continues with submitting clean claims. This up-front work involves a considerable amount of manual, repetitive processes, including ensuring all payer-specific coding guidelines are met, verifying proper eligibility and authorizations, providing detailed clinical documentation, following agreed-upon timelines and more, all of which work together to reduce denials. 

John Mordach, the chief financial officer at Jefferson Health and Jefferson Health Plans, agrees. He says it is crucially important to prioritize work such as the pre-approval process and insurance verification. He also notes it is important to make sure the disease group, treatment category and insurance match up, and says if this work is not done at the beginning, denials are bound to follow.  

While these tasks can be time consuming and take a toll on staff capacity, many of them can be automated to reduce errors, free up staff for more high-touch areas and shorten the claim cycle so providers can get reimbursed faster.     

Mordach states that Jefferson Health has implemented some technology to combat denials. “One of the things we’ve done is adopt more technology, particularly with robotic process automation (RPA) and artificial intelligence,” he said. Mordach explains they are using RPA to pull information and data that is then quickly sent back to the insurance plan when they believe something has been erroneously denied. This proactive approach has resulted in prompt responses and recovery of funds initially denied or rejected. 

The role of AI and automation in denial management 

AI and automation can play a pivotal role in denial prevention, management and recovery by streamlining processes, reducing errors and enhancing efficiency. Below are four ways in which automation can be used by providers. 

1. Preventing denials
   AI can help prevent denials by identifying potential issues before claims are submitted. For instance, AI can analyze historical data to predict which claims are likely to be denied based on factors such as diagnosis, treatment and insurance plan. This allows providers to address potential issues proactively, reducing the likelihood of denials.
2. Automating routine tasks
   Automation can handle repetitive tasks such as insurance verification and pre-authorization, reducing the risk of human error. This not only improves accuracy but also frees up staff to focus on more complex tasks. 
3. Analyzing denial patterns
   AI can analyze patterns in denials, identifying common reasons for rejections. This information can be used to improve processes and training, reducing the rate of denials over time. 
4. Streamlining appeals
   When denials do occur, AI can help streamline the appeals process. AI can automatically generate appeal letters based on the specific reasons for a denial, saving time and improving the chances of successful appeals.