Every CFO and Coding Director has likely had the same thought at some point:
There has to be a better way.
Not because they are looking to cut corners. Quite the opposite. The pressure to improve coding accuracy, maintain audit readiness, and maximize reimbursement has never been greater. At the same time, organizations are facing rising labor costs, staffing shortages, increasing regulatory scrutiny, and growing pressure to do more with fewer resources.
For years, the industry’s answer to these challenges has been straightforward: add another review.
If one coding pass improves accuracy, a second pass improves it further. If a second pass increases confidence, a third validation creates even greater assurance. Over time, multi-pass coding became the accepted standard because it worked. Organizations could improve accuracy and reduce risk by layering reviews, quality checks, and vendor validations.
But while the process works, it comes with tradeoffs that every healthcare leader understands.
Multiple passes require more people, more time, and more money. They create operational complexity, contribute to reviewer fatigue, and often introduce inconsistent interpretations between reviewers. Coding teams spend valuable hours reexamining charts that have already been reviewed, while leadership continues to absorb the growing costs associated with maintaining these layered workflows.
The industry accepted these challenges because there appeared to be no viable alternative.
After all, if accuracy and defensibility couldn’t be achieved in a single pass, then multiple passes were simply the price of confidence.
Until now.
The missing piece has never been effort. It has been technology.
Traditional AI solutions have helped automate portions of the coding process, but they have largely functioned as recommendation engines. They can identify diagnoses and suggest potential codes, but they often struggle to explain exactly why a code should be submitted or whether the supporting documentation will withstand audit scrutiny.
In today’s Medicare Advantage environment, that distinction matters.
Organizations are no longer looking for coding accuracy alone. They are looking for coding accuracy that is explainable, traceable, and defensible. Finding a diagnosis is important. Proving it is becoming essential.
This is where Neuro-Symbolic AI changes the conversation.
RAAPID’s OnePass solution is powered by Neuro-Symbolic AI, a glass-box architecture that combines neural language understanding with symbolic clinical reasoning. The neural component understands and interprets clinical documentation much like an experienced reviewer. The symbolic component applies clinical logic, coding guidelines, and reasoning pathways to validate the findings.
Together, they create something traditional AI has struggled to deliver: transparency.
Every coding recommendation can be traced back to supporting clinical evidence. Every diagnosis is linked to a clear rationale. Every submitted code is paired with a MEAT-based evidence trail designed to support audit readiness and defensibility.
This is more than a technological advancement. It represents a fundamental shift in how coding confidence is established.
Historically, confidence was assembled across multiple reviews. One reviewer identified a condition. Another validated it. A third review provided additional assurance. The process was built on layers because no single step could consistently deliver the level of accuracy and defensibility organizations required.
Neuro-Symbolic AI changes that model by embedding explainable reasoning directly into the first review. Instead of building confidence across multiple passes, confidence is established at the point of identification.
The result is what RAAPID calls OnePass—a workflow that delivers audit-ready coding output with a single coder review.
That’s a bold claim. And understandably, many organizations are skeptical.
Healthcare leaders have heard ambitious AI promises before.
Which is why results matter.
In independent customer engagements, RAAPID demonstrated 92% AI-only coding accuracy. When paired with a single coder review, accuracy exceeded 98%, while maintaining 100% MEAT-based defensibility for submitted codes.
The implications extend beyond coding performance. Organizations can reduce review cycles, lower operational costs, decrease reviewer fatigue, simplify vendor management, and accelerate chart completion—all while maintaining the accuracy and defensibility that Medicare Advantage programs demand.
Most importantly, coding professionals can focus their expertise where it matters most: evaluating complex clinical scenarios rather than repeatedly reviewing the same charts through multiple layers of validation.
For years, healthcare leaders have asked the same question:
“How many passes do we need to achieve confidence?”
Neuro-Symbolic AI introduces a different question:
What if confidence no longer requires multiple passes?
For CFOs, HIM leaders, and Coding Directors, that may be the most important question facing risk adjustment programs today. Because the future of coding may not belong to organizations that review charts more times. It may belong to organizations that can achieve accuracy, transparency, and defensibility the first time.
Take the RAAPID OnePass Challenge
The concept of achieving audit-ready coding with a single pass sounds ambitious. We understand that. That’s why we invite organizations to see it for themselves.
Take the RAAPID OnePass Challenge and discover how Neuro-Symbolic AI delivers accurate, explainable, MEAT-backed coding results that reduce complexity while increasing confidence.
Because the question is no longer whether multiple passes work.
The question is whether you still need them.
RAAPID OnePass Challenge
