State of denial and the power of AI

Claim denials remain one of the most persistent and costly challenges facing healthcare systems. According to a study published last year by the Healthcare Financial Management Association, “out of $3 trillion in total claims submitted by healthcare organizations, $262 billion were denied, which translates to nearly $5 million in declined dollars, on average, per provider”.

The outbreak of the Covid-19 pandemic exacerbated the problem, with national refusal rates increasing by 11% between July 2019 and June 2020.

The fact that the majority of denied claims are preventable is only one aspect of a much deeper problem. Redrafting claims takes time, money and resources.

A healthcare system’s quagmire of claims typically stems from several underlying internal and external issues, including:

  • Complex and ever-changing undocumented adjudication rules between payers make effective claims management extremely difficult.
  • Errors and inefficiencies in one area, such as benefit eligibility, can lead to problems downstream in the revenue cycle.
  • Inefficiencies in handling the growing number of pre-authorizations can easily drive up a supplier’s denial rate.
  • Relying on manual processes is both expensive and time-consuming.

Solutions like robotic process automation can work well for simple, repetitive tasks, but to truly maximize reimbursement, healthcare systems should explore the potential of artificial intelligence (AI).

AI provides the power of automation, but takes it to a whole new level by adding the predictive capabilities, continuous learning, and insights needed to proactively prevent claims from being denied before they are. submitted, accelerate and prioritize human edits, and take advantage of advanced analytics. to uncover actionable insights from a healthcare system’s own data to optimize claims management across the revenue cycle.

Level the playing field

Most claims management technologies are designed to prevent claims that may be denied by payers, based on well-documented payer rules, from ever being delivered.

A billing service will apply standard or custom changes to a claim, based on what a payer has publicly stated they will use to settle it. Once the changes are made, the clean claim is sent to a clearing house or payer for adjudication.

Unfortunately, the rules payers use to adjudicate claims are constantly evolving and often change without notice. This leaves suppliers playing a perpetual game of catching up.

AI has the potential to level the playing field by making highly accurate calculated predictions about the likelihood of a request being denied, including undocumented payer change denials. Therefore, giving providers real-time capabilities to monitor and, if necessary, take immediate action to correct claims before submitting them to maximize reimbursement. The key to doing this successfully requires that the AI ​​modeling be based on the provider’s own claims and payor payouts, not a generic pool of mixed provider claims and payor payouts.

Focus on recoverable receivables

The energy and resources expended in appealing denied claims are often based on the dollar amount rather than the likelihood of recovery.

It seems intuitive to focus on recovering a $100,000 claim rather than a $5,000 claim. However, if the odds of an appeal for the former are extremely low, claims departments are simply wasting time and money trying to collect revenue that is unlikely to be recovered.

Here, AI can make a real difference through sophisticated claim denial and successful modeling of new submissions/appeals. By going through the provider’s own record systems, an AI platform can analyze the history of the claim in question, as well as similar claims to uncover information such as the success of calls made and percentage of reimbursement. recovered.

This level of granularity gives providers the ability to score denials based on their likelihood of reimbursement, a methodology that will bear more fruit than focusing on complaints just because they are expensive.

End-to-end claims management

AI platforms can do more than just manage complaints. At the right scale, AI can solve many problems and inefficiencies that have significant downstream impacts on the revenue cycle. The two most obvious applications relate to patient eligibility and prior authorizations.

Patient access can be a major source of error, especially around eligibility. AI can bring intelligence to real-time eligibility checks, delivering higher levels of automation, accuracy, and efficiency.

Prior authorizations and medical necessity are also a common source of claims issues. As the number of pre-approvals increases, the need to improve claims management processes becomes more critical. The potential of AI in this area includes automating key aspects of prior authorization processes, including determining the need for prior authorization, highlighting and fixing medical documents and images relevant, and monitoring the status of permissions.

Another benefit of AI is its ability to apply learning to prediction. Smart platforms can then flag present and future claims at risk of denial based on insights they surface from oceans of historical remittance data. More than flagging potential roadblocks, AI programs can use its analysis to identify root causes in the claims workflow.

Staff impact

One of the downsides of AI technology is the pervasive idea that the technology is a threat to human job security.

These concerns are valid, but the intentions of AI deployment can be misinterpreted. In today’s hospital billing environment, staff are increasingly being pressured to do more with less. An overstretched staff leads to the fraying of essential efficiencies.

In the case of claims management, AI is best viewed as a staff enhancement, handling large-scale, repetitive tasks and performing complex analytics, while employees focus on reworking the claims most likely to fail. get a refund.

Provider organizations may never fully eliminate their denied claims. However, the power of AI can help suppliers recover more of what is owed to them, better anticipate at-risk claims, and identify claims worth appealing.

Photo: Michail-Petrov-96, Getty Images

Comments are closed.