Innovator Awards Vendor Finalist: Availity

March 14, 2023
Artificial intelligence and machine learning tools have the potential to move your organization from costly denial management to streamlined denial prevention.
2023 Innovator Winner Story Images Availity
2023 Innovator Winner Story Images Availity
2023 Innovator Winner Story Images Availity
2023 Innovator Winner Story Images Availity
2023 Innovator Winner Story Images Availity

Artificial intelligence (AI) has been healthcare’s next big thing for years. While AI’s application in clinical care and pharmaceuticals has received the most buzz, the technology’s potential impact on the $43 billion spent each year on healthcare’s revenue cycle could be equally transformative.   

Hospitals and health systems’ already-narrow operating margins have been thrashed by a perfect storm of external and internal threats, including the ongoing pandemic, chronic staff shortages, rising expenses, and an inflationary economic environment.  

More than ever, provider organizations need to maximize reimbursement opportunities. Unfortunately, denied claims—and the associated adjudication process—remain one of the leading causes of revenue shortfalls in provider organizations.

According to a November 2022 report from Crowe LLP, a global consulting firm, average denial rates grew from 10.2% in 2021 to 11% in 2022. Other studies show that the average cost to rework a claim is $25 and typically takes about 71 minutes. It also costs about $118 per denied claim to appeal.

However, the value of the unpaid claim and the time it takes to rework it are only part of the overall impact on a health system’s bottom line. Many provider organizations invest too heavily in reactive solutions—point technologies, staffing surges, and complex appeal processes. These tools may help “manage” denials, but true cost savings lie in prevention.

Tools with AI capabilities address many of the time-consuming, resource-intensive, and costly processes associated with claims editing and denial management. But the true value of this technology is its ability to analyze claims for errors before routing them to the payer. Many systems offer packaged or custom edits to claims before submission, but these edits are built retrospectively, requiring costly analysis to determine the root cause of the denials, and ongoing maintenance as payers’ adjudication rules shift in response to external forces. Telehealth claims adjudication, for example, changed rapidly during the early days of the COVID-19 pandemic.

The ideal system would analyze claims from multiple providers, going to multiple payers, and spot trends that will likely lead to denials. Applying AI to the constantly changing stream of data removes the manual writing and maintenance of edits and allows health systems to react to changes more quickly – before the claims are submitted and a new batch of denials has to be analyzed.

The potential of predictive editing

Providers want more effective ways to identify and prevent denied claims so they can reduce administrative rework and lost revenue associated with them.

Predictive editing uses an AI algorithm that focuses on the subset of denials that are most likely to be avoidable and correctable. The algorithm’s predictive capabilities lie in its ability to analyze claims data across a broad network of provider organizations, as well as policies specific to individual health plans.

The solution will return the predicted Claim Adjustment Reason and Remark (CARC & RARC) if the likelihood of denials is very high; the model only returns edit errors when there is 98 percent confidence that the claim will be denied.

As a result, providers can:

·        Reduce the administrative cost of reworking claims and improve revenue cycle performance.

·         Increase edit coverage by capturing complex, payer-specific edit scenarios that are beyond the scope of traditional front-end edit engines.

·         Reduce the administrative effort of maintaining manual rules.

·         Allow providers to save on implementation costs because predictive editing can be used within the provider’s existing edit/error management tools, especially if they use Availity Essentials Pro.

The path toward a sustainable and healthy revenue cycle requires tools, insights, and analytics to help providers submit claims right the first time. Artificial intelligence and machine learning tools have the potential to move your organization from costly denial management to streamlined denial prevention.

Linda Perryclear is Senior Director, Product Management, at Availity. To learn more about predictive editing and other revenue cycle services, please contact her at [email protected] or visit

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