AI Revenue Intelligence Market Map: Turning revenue insights into millions in savings
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.
In healthcare operations, a clear and accurate picture of revenue and accounts receivable can mean the difference between profitability and losing millions of dollars.
High level visibility into the revenue cycle can enable workflows like:
Understanding common sources of revenue leakage across the system—denials, audits, takebacks, concurrent denials, patient payments, etc.
Monitoring and identifying changes in performance across service codes, practice sites, providers, and denial reasons.
Evaluating collection strategies and patient segmentation.
Reviewing contract performance to identify trends in underpayment or inform re-negotiations.
Forecasting and understanding the financial health of the system.
However, because revenue cycle management (RCM) often involves a combination of end-to-end systems and a variety of point solutions focused on specific areas, getting a comprehensive picture becomes a challenge.
One dashboard to rule them all
The greatest hurdle to having a well-modeled version of the full revenue cycle is data integration: pulling in claims data, clinical data and codes, and data from other billing systems. Once that is accomplished, it’s much easier to not only get a comprehensive view of the state of revenue and accounts receivable, but to run modeling and see where improvements can be made. Some of the first vendors to operate in this category include MedeAnalytics and Visiquate.
(Note: There are numerous end-to-end RCM products on the market, and within these, data integration is essentially baked in. One of the key value propositions of revenue intelligence products is the integration of data across vendors and products, so we’ve purposefully excluded end-to-end vendors in this category.)
Beyond data integration, dashboarding is relatively straightforward and offers little value if it doesn’t provide actionable information. It’s easy to show denial rates over a timeline, but it’s much more interesting to look at the reasons for the denials and try to understand what is driving changes.
Catching revenue leakage with AI
Going a step further, we’re seeing some vendors focus on specific tools to improve denial management and other sources of revenue leakage (Adonis Intelligence, Deloitte Revenue Intellect and Rivet Revenue Diagnostics). This is where machine learning (ML) can come into play.
When payers change their adjudication engines, impacting claims denials, ML is effective at spotting patterns and identifying potential actions to take in the process. For instance, Anomaly analyzes payer trends and predicts denied claims and next-best actions to improve payments. We’re also seeing some vendors start to use LLMs to produce action reports for specific stakeholders and roles based on intelligence derived from the data (Sift Rev/Track).
It’s worth noting that there’s a fundamental asymmetry between payers and providers. Payers can make a small tweak to their adjudication engine and it reduces their costs dramatically, while it takes a long time for providers to notice the changing trend in denials. However this increase in denials adds up over time and becomes too expensive to not optimize. Part of the value of ML is in spotting trend changes early.
Solutions go big or go home
Because data integration and tuning ML models to a specific provider can be expensive, most of the players in this market cater to hospitals and health systems with bigger budgets. We identified two solutions that do focus on ambulatory practices, though (Etyon and RevOps Health).
While we see substantial value in having the high-level view and feedback mechanism to improve other parts of the revenue cycle model, insights are worthless without specific concrete actions. Better still is a machine that is “self-tuning”—uncovering and acting on the insights in a single self-contained workflow. For that reason, we think the future of revenue cycle management is integrated, end-to-end systems that can reason along the longitudinal journey of a claim, from the first eligibility check through adjudication and patient payments.