Organic Growth Podcast: Unlocking Stuck Revenue: How AI Is Fixing Healthcare's RCM Bottlenecks – Image for illustrative purposes only (Image credits: Unsplash)
Healthcare staff often spend hours dialing payers, chasing down claim details that determine whether bills get paid on time. This manual grind leaves revenue trapped in backlogs, straining operations and squeezing margins for providers already under pressure. A recent podcast discussion at the McGuireWoods Healthcare Private Equity & Finance Conference shed light on how voice AI is dismantling these barriers, offering a path to faster collections and reduced administrative headaches.
Revenue Trapped in Administrative Limbo
Providers frequently encounter revenue that appears lost but is merely delayed. Claims linger past 30 or 60 days, prompting write-offs or outsourcing to revenue cycle management firms. These firms collect what they can on a contingency basis, yet much remains unrecovered due to insufficient follow-up.
Backlogs build quickly in large health systems or provider groups. Without timely intervention, aged accounts receivable become costly burdens. The core issue lies not in the claims themselves but in the slow retrieval of critical data from payers, such as status updates or eligibility details.
Phone Calls: The Hidden Drag on Efficiency
Manual interactions dominate tasks like claims follow-ups, prior authorizations, benefit verifications, and denial appeals. Staff read scripts from screens for hours, risking errors from misread numbers or letters. Payers sometimes disconnect abruptly, forcing repeated attempts that multiply delays and costs.
These workflows demand significant labor. In a tight hiring market, scaling call centers proves challenging and expensive. Providers historically responded by adding headcount, but current constraints make this untenable for growth.
One concrete example emerged from a revenue cycle firm focused on behavioral health. Its leaders identified payer-provider communication as the primary bottleneck after handling countless phone-based hurdles.
Voice AI Steps In to Automate the Grind
Companies like SuperDial deploy AI-driven voice technology to handle these repetitive calls at scale. The system navigates payer lines, extracts data accurately, and feeds it back into electronic health records or internal platforms. Customers pay only for completed transactions, regardless of whether it takes one call or several.
SuperDial originated five years ago within a behavioral health RCM operation. It has since expanded to direct provider groups and even secured its first payer client. The tool covers claim status checks, denials, prior authorizations, and benefits verification – tasks long reliant on human effort.
Cost Savings Meet Revenue Gains
Operators, particularly those backed by private equity in management services organizations or dental service organizations, view AI as a dual win. It delivers cost reductions – potentially 50% return on investment compared to call center staff, even offshore ones – while clearing backlogs to boost top-line revenue.
As industry margins tighten amid competition from scaling giants, efficient back-office scaling becomes essential. AI not only cuts administrative burden but also recovers funds from outstanding accounts, enhancing overall financial health.
- Automates high-friction tasks like prior authorizations and claims status.
- Reduces human error in data entry and retrieval.
- Enables growth without proportional staffing increases.
- Integrates with clearinghouses and payer portals for comprehensive visibility.
Toward a Future of Seamless AI Interactions
Revenue cycle management intersects every stage of healthcare workflows, from patient intake to final payments. Gains here ripple outward, positioning RCM as a prime entry for broader AI adoption. Brian Plamondon, Director of Partnerships at SuperDial, captured the essence: “Revenue doesn’t disappear – it gets stuck in the system.”
The long-term vision involves AI systems communicating directly with one another, eliminating human involvement in payer-provider exchanges. While this remains in development, early adopters already experience accelerated processing and reliability. For providers navigating labor shortages and rising demands, such tools promise sustainable scaling and financial stability.
