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RAAPID OnePass Challenge

Audit-ready risk adjustment coding in a single review workflow

RAAPID OnePass transforms risk adjustment coding from a multi-pass validation process into a single-review, evidence-backed workflow.

Powered by the RAAPID Neuro-Symbolic AI platform, every diagnosis is linked to chart evidence and supported by a MEAT-backed rationale built for audit readiness.

Take the RAAPID OnePass Challenge

multiple reviews from other vendor

  • Pass 1 · Find the codes

review 

  • Pass 2 · Validate

review

  • Pass 3 · Quality check

review 

  • RAAPID OnePass
audit-ready
E11.22
  • MEAT evidence linked in note
  • RAAPID OnePass

audit-ready

Reads the chart Code with Evidence Justify with Confidence

E11.22

  • MEAT evidence linked in clinical note

Defensible

The problem

Why risk adjustment coding still takes multiple passes

Most teams still rely on multiple review cycles to get comfortable with coding accuracy:

1

One pass to identify potential diagnoses

2

Another to validate evidence and MEAT

3

Another before submission or audit prep

That process exists because most tools can suggest codes, but they can’t reliably show the evidence, explain the rationale, and defend the result. The result is predictable:

more coders

more hours

more spend

more burnout

The shift

What RAAPID OnePass changes

RAAPID OnePass helps risk adjustment teams move from multi-pass coding and validation to single-review, evidence-backed coding.
With OnePass:
  • The chart is reviewed once, not repeatedly routed through multiple hands
  • Each diagnosis is linked back to supporting evidence in the record
  • Each recommended code includes a MEAT-backed rationale
  • Reviewers can see why a code was suggested, not just that it was suggested
  • Submissions are prepared with audit defensibility in mind from the start

The engine

How RAAPID can do this

RAAPID combines clinical language understanding with rules-based clinical reasoning in a Neuro-Symbolic AI architecture:
  • The AI reads the chart and identifies relevant diagnoses and evidence
  • The symbolic reasoning layer evaluates whether the diagnosis is supported by coding logic and MEAT criteria
  • The result is a transparent recommendation with evidence and rationale attached
NLP extracts. Traditional AI predicts. Neuro-Symbolic AI explains, proves, and stands up to an audit.

The proof

  • Over 98% accuracy with a single coder review (RAAPID internal benchmark)
  • 92% AI-only coding accuracy, independently validated
  • A MEAT-backed evidence trail on every submitted code
  • One coder review, not many

The Payoff

The OnePass Advantage

Risk Adjustment

Stop routing the same charts through multiple sets of hands. Coders spend their expertise on complex cases, not settled ones, and every submission lands audit-ready.

Finance

Fewer review cycles mean lower cost per chart and fewer vendors to manage. Defensibility is built in, not bought back through remediation.

Compliance

Every diagnosis carries an evidence trail: what was found, why it qualifies, and where it lives in the record. When a RADV notice arrives, the documentation is already in place.

RAAPID OnePass Challenge

The question isn’t how many passes you need. It’s whether you still need them.
See the evidence behind every code.

One Pass Landing Page

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