AI Denials Management Market Map: Recovering lost revenue with robots?
This is part of Elion’s weekly market map series where we break down critical vendor categories and the key players in them. For more, become a member and sign up for our email here.
Despite confirming eligibility, checking benefits and receiving prior auth (if required), 15% of claims are still denied. With the cost to rework or appeal a denied claim $25 for practices and $181 for hospitals, 65% of denied claims are never re-submitted. As a result, 35% of hospitals report at least $50M in annual lost revenue from denied claims. (This does not even include lost revenue from underpayment.) In short, there’s a huge opportunity for vendors who can solve this pain point.
Jumping through hoops
After submitting a claim, it is bucketed into one of the following categories:
Claims with no response yet
Claims with a non-payment response
Rejected claims
Denials management is focused on claims with a non-payment response. Teams typically work on denials by looking at the specific reason for denial (i.e. lack of eligibility, lack of authorization, lack of provider credentialing, duplicate claims, coverage status, medical necessity, requests for medical records, or other responsible payers) and take action based on those specific reasons.
The legacy workflow looks something like:
Review denial notification from payers and the specific reason for denial, logging and tracking it.
Review original claim submission and supporting documentation, perform “root cause analysis” to identify the specific cause of denial.
Gather additional information from clinical documentation or even from care teams if needed. Correct errors.
Submit an appeal package and detailed letter within a given timeframe.
Follow up with the payer, checking appeal status until resolved.
The promise of AI
AI offers potential optimizations across each step of the denials management process. Products in this category perform functions including:
Automating administrative steps, like checking status, syncing updates to the EHR, and document submission. (e.g. Rivet Claims Resolution, Crosby Health)
Prioritizing rework that needs to be done by a human based on a number of factors including likelihood of being overturned and paid. (e.g. Sift Denials)
Using GenAI to make corrections to the denied claims, such as updating coding, providing additional documentation, or correcting patient information. (e.g. Crosby Health)
Using GenAI to find underpayments even for claims that are paid (e.g. MDClarity Revfind, Rivet Payer Performance)
Vendors in the revenue cycle automation category should also be considered in the context of denials management, because the workflow is conducive to intelligent classification and workflow automation.
Additionally, there are a number of end-to-end RCM providers who also offer AI-enabled denial management modules that can be used both as part of the end-to-end platform, or used in conjunction with other products. These include Change Denial and Appeal Management, Datavant Denial Management, Experian Denial Management, MedMetrix Denials Recovery, and Waystar Denial and Appeal Management.
A substantial portion of effective denial management is process-based: finding the systematic reasons for denials and fixing them before they become an issue. As such, there is significant potential for symbiosis between AI tools across the revenue cycle.
We believe the future for AI in RCM will be predicting which claims will be denied before they are submitted—even as early as the point of care. To do this, vendors need to start thinking longitudinally about how each step affects the probability of payment, and optimizing backwards. We see this developing substantially over the next few years.