The Revenue Cycle Needs Surgery, Not Band-Aids

Whether you view it as an AI gold rush or an AI bubble, in healthcare, the ambient scribe market is still on fire. Capturing astronomical capital investment, such as Abridge announcing a $300M funding round in June, ambient technology has quickly become one of the most impactful applications of AI in healthcare. As enthusiasm and funding grow, so do investor expectations and market saturation. This is evident with EHRs launching their own solutions. As startups are competing to differentiate themselves, established companies are learning from their vendor partners to develop their own tools.

To demonstrate ROI beyond reducing physician burnout and providing the financial returns their partners expect, ambient scribe companies are now expanding their focus beyond clinical documentation to revenue cycle solutions. It’s a logical next step for these companies to follow the money, especially as the revenue assurance market is projected to reach $1.6 billion by 2033. 

We’ve seen this pattern before with the telehealth boom in 2020. When EHR vendors and health systems launched their own video visit tools, telehealth companies were forced to adapt. To stay relevant, they expanded into chronic care management, remote patient monitoring, and behavioral health. When faced with the threat of becoming obsolete, they evolved from being seen as simply a feature into comprehensive care platforms.

Still, in many ways, scribes entering the revenue cycle is the equivalent of putting a Band-Aid on a wound that clearly needs surgery. As we learned with the telehealth boom, features will get absorbed, while foundations will endure. Scribes can’t keep taking on adjacent features in hopes of maintaining their relevance. 

While these product expansions may help fill small gaps between documentation and billing, scribes were never designed to fix the root causes of revenue leakage. By themselves, they cannot resolve the systemic flaws facing our system, such as coding inefficiencies, claims denials, and administrative bottlenecks created by prior authorization delays, all of which collectively cost health systems billions of dollars in revenue every year. 

Operating margins are razor-thin, denials are up 52% year-over-year, and every claim denial poses a threat to a health system or provider group’s ability to operate, and in some cases, remain open. In this environment, accurate documentation is only half the battle; the real challenge lies in ensuring every encounter is coded and reimbursed correctly the first time around. 

It is not just about automating documentation, but deploying compliant and transparent AI solutions that can interpret payer requirements, reduce hallucinations, and align with both organizational and payer-side processes. The goal shouldn’t just be to capture what was said in the exam room, but to ensure every clinical detail translates into accurate and compliant codes.

Any tool that still requires a human coder to check for accuracy isn’t solving the real problem. Most AI scribes are designed for one-to-one transcription, converting physician conversations into written notes, rather than possessing the specialized logic required to generate accurate billing codes. Effective AI coding involves more than simply transcribing care accurately; it must also understand payer requirements, documentation patterns, and clinical context. As long as revenue-cycle tools continue to rely on human review and correction, the most costly and time-consuming part of the process remains untouched.

As they currently stand, scribe-built revenue cycle tools are quick fixes, not long-term solutions. Organizations need to focus on tackling the real cause of denials rather than just the symptoms. 

Healthcare organizations need to invest in technologies that apply AI directly to the coding process itself. These solutions allow organizations to interpret clinical documentation in real-time, apply the correct logic and payer configurations, and generate compliant claims automatically, eliminating the need for a lengthy manual review process that only slows down reimbursement. 

I should make it clear I don’t consider AI coding solutions and ambient scribes as competitors. In fact, I believe aligning the two represents one of the most significant opportunities for partnership in the health technology space. 

Ambient scribes are fantastic at capturing the patient story; AI coding ensures that the story translates into the organization being paid on time and in full. When used together, they can close the loop between clinical care and financial sustainability. But on their own, scribes can’t solve the revenue crisis.

AI coding solutions bring something to the table that scribes don’t: the ability to analyze documentation, provide insights to physicians to help them phrase or capture details in ways that reduce the risk of claim denials. While ambient scribes make documentation easier, AI coding makes reimbursement more accurate and efficient. This isn’t just a technical limitation, too; it’s structural. The person or tool responsible for documentation shouldn’t necessarily be the one responsible for validating it. AI coding can provide clinicians with real, rules-based feedback on how to document more clearly or completely, but the reverse isn’t true; scribes can’t guide coding because they don’t understand coding logic. 

As we see companies release more solutions targeting the revenue cycle, it’s time to distinguish excitement from impact. The future won’t be defined by who builds the best scribe with the flashiest add-on, but instead will be shaped by those who close the loop between clinical documentation and reimbursement.

Photo: Afry Harvy, Getty Images

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