The problem with AI in the revenue cycle is transparency. It is powerful, but it usually operates as an unpredictable black box. In risk adjustment, you simply can’t afford to guess how an algorithm arrived at a billing code. You need a glass box. You need absolute defensibility. Here is how that is finally becoming a reality.
Healthcare IT Today sat down with Chetan Parikh, Founder and CEO of RAAPID, to explore the evolution of risk adjustment technology. We discussed the challenges of relying on standard NLP and why organizations need technology that balances accurate coding with strict regulatory compliance.
What This Conversation Revealed
- Neuro-symbolic AI offers a glass box approach. By combining large language models with proprietary knowledge graphs, organizations gain high accuracy and fully defensible evidence without the risk of hallucinations.
- AI reduces the mental load on medical coders. Highly accurate AI tools allow coding teams to stop sweating the small details and start operating at the top of their license.
- Technology must balance revenue and compliance. The right AI ensures providers get paid for the services they deliver while preventing the regulatory risks of over-billing.
Neuro-Symbolic AI Provides Defensible Evidence
Standard natural language processing casts a wide net but often struggles with precision in complex clinical documentation. Health IT leaders know that adopting large language models brings risks of hallucination, making pure generative AI difficult to trust for revenue cycle applications. The solution, according to Parikh, lies in neuro-symbolic AI.
Parikh explained how RAAPID addresses this industry hurdle by marrying large language models with proprietary knowledge graphs. He noted that their technology focuses on “taking full advantage of the large language models and at the same time making sure that we are not hallucinating”. Parikh further detailed that this approach is all about “converting from a black box to a glass box, where everything is defensible and evidence based.”
Elevating the Role of Medical Coders
Finding and retaining highly skilled medical coding talent is a persistent challenge for provider organizations. When legacy NLP systems only deliver moderate out-of-the-box accuracy, human coders are forced to spend excessive time verifying outputs.
However, with RAAPID’s neuro-symbolic powered AI systems, organizations can achieve more than ninety percent accuracy. This dramatically improves the entire workflow for coding staff. Parikh highlighted this by stating that “when you have an AI that is as accurate as 91 – 92% out of the box, the coder’s mental load is significantly reduced, and the coders are now operating at the top of the license rather than they trying to identify everything.”
Hitting the Sweet Spot Between Revenue and Compliance
Risk adjustment requires walking a tightrope. If an organization under-codes, they will not capture the true value of care delivered. Conversely, aggressively capturing codes without sufficient documentation triggers intense scrutiny from federal regulators.
“If your AI is unable to identify codes that are truly billable, then you did the work, you provided the service, but you are not getting paid for it,” Parikh summarized. “But you have to make sure to not be overcoding and overbilling.”
Health systems need a middle ground where they capture accurate reimbursement while remaining securely within regulatory boundaries.
The Bottom Line
Risk adjustment technology needs to move beyond good-enough AI with opaque models. As organizations evaluate new AI tools for their revenue cycle, the focus must be on accuracy, defensibility, and operational efficiency. Implementing AI that provides clear evidence pathways, like what RAAPID offers, protects the organization from compliance risks while ensuring fair reimbursement for care delivered.
What Healthcare IT Leaders Are Asking
What is neuro-symbolic AI in healthcare? Neuro-symbolic AI combines the pattern recognition capabilities of large language models with the structured logic of proprietary knowledge graphs. This hybrid approach provides the broad contextual understanding of generative AI while anchoring the outputs in factual, evidence-based rules to prevent hallucinations.
How does AI impact medical coding compliance? Advanced AI improves coding compliance by linking suggested codes directly to documented clinical evidence. By surfacing only defensible codes, the technology helps organizations avoid over-billing while ensuring they capture all appropriate revenue for services rendered.
Why is a “glass box” approach important for risk adjustment? A glass box approach allows human auditors to see exactly how an AI model arrived at a specific coding conclusion. In highly regulated areas like risk adjustment, being able to trace a suggested code back to the exact clinical documentation is essential for defending claims during audits.
Learn more about RAAPID at https://www.raapidinc.com/
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RAAPID is a sponsor of Healthcare Scene
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