How AI Powers Better EBITDA Via Automation

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RCM companies face many difficulties at the moment: a tight labor market for experienced medical billing staff, increased complexity in the revenue cycle leading to claim denials, and ever growing patient deductibles causing patient accounts receivable (AR) headaches. All of these challenges directly impact staff productivity both in terms of utilization and efficiency. And when staff utilization and efficiency are negatively impacted, EBITDA suffers. 

Take an RCM company with $12 million in revenue but only 15% EBITDA margin. Since most RCM expenses are tied up in labor, in order to expand EBITDA margin to 20%, the solution must directly address staff productivity. Furthermore, the solution must take into consideration the differences in ROI for the various duties for which an RCM company is contracted. For instance, patient AR and collections require an outsize effort per dollar captured compared to higher value duties like handling claim denials. By some estimates, it’s four times more costly for an organization to collect from a patient versus an insurance payer.*  Also, the extremely constrained 2021 labor market has put a strain on RCM companies seeking to grow as the healthcare industry recovers from the pandemic. Not to mention, the U.S. Bureau of Labor Statistics projects a 9% increase in medical records and health information jobs through 2030, exceeding the projected average growth for all jobs in the economy. Finally, the decentralized workflows and heterogeneous data environments of a typical RCM company’s client portfolio are costly to manage and prone to human error.

The answer is to centralize patient billing and collection efforts through AI-enabled data integration and automate patient engagement with AI-driven patient outreach. At one level, AI fills the integration gaps between the many different EHR systems an RCM company has to manage each and every day. But more critically, the Raxia AI platform allows that data to be centralized so that processes can be automated in a uniform and consistent manner, i.e. gathering information needed for patient billing and outreach and then, in turn, centrally automating the posting of payments, entering data, and even printing and mailing paper statements.

Similarly, technology can be leveraged to dramatically reduce the effort required to manage patient outreach while simultaneously improving patient AR. The Raxia Decision Engine uses contextual data to infer the most effective way to individually communicate with each patient so as to activate engagement and motivate them to fulfill their financial obligations to their providers.

The Raxia AI platform will automate critical patient payment workflows, decreasing staff training and staff effort. This increase in revenue cycle efficiency leads to lower labor costs which ultimately equates to higher EBITDA margins, the key metric for RCM company financial performance. 

* Rosso, Anne. “The Rise of Self-Pay Accounts.” Collector, Feb. 2015, pp. 24-29,

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