Most vendors use AI that suggests code but can’t explain why. RAAPID’s Neuro-Symbolic AI provides transparent clinical reasoning—linking every HCC to specific MEAT and Condition Evidence in the chart. This creates audit-defensible documentation, not just code suggestions. We also flag unsupported codes that other vendors ignore. And we’re the only vendor guided by a former CMS auditor who knows what actually survives RADV scrutiny.

Our Risk Adjustment Platform uses Neuro-Symbolic AI to combine the speed of neural networks (pattern recognition) with symbolic reasoning (explicit clinical logic). This means RAAPID can identify potential HCCs like other AI tools, but it also applies clinical reasoning rules to validate them against MEAT criteria. The result is AI that shows its work—every suggestion includes the clinical reasoning and evidence behind it.

RAAPID validates that every diagnosis is tied to a real patient encounter where the condition was evaluated, monitored, assessed, or treated. Diagnoses found in charts but not linked to qualifying encounters are flagged—helping you avoid submitting codes that won’t survive audit scrutiny.

Our customers typically see 60-80% productivity improvement in their coding teams and achieve 98-99% coding accuracy with every HCC supported by documented MEAT evidence. One multi-state MA plan improved from 70% accuracy with their prior vendor to 98% with RAAPID.

4-6 weeks from contract signing to go-live. Our team handles data integration, model configuration, and coder training. You’ll see 92% accuracy from day one, improving to 99% as the AI learns your specific documentation patterns.

Yes. RAAPID integrates with all major EHR and risk adjustment platforms. We deploy within your Azure tenant for data sovereignty and can use your existing Microsoft MACC credits. Our API-first architecture fits into your workflow.

Yes. Many organizations start with a coding accuracy assessment before committing to an ongoing program. We review a sample of your charts and deliver a detailed report on accuracy, documentation quality, and audit readiness.

 No. RAAPID works with health plans, provider organizations, hospitals, and ACOs. Our AI platform and coding services adapt to your specific volume and use case.

The best risk adjustment software validates every HCC against encounter-linked clinical evidence, flags unsupported codes for removal, and produces audit-ready documentation. Look for AI that is explainable and governable, not a system that only adds codes without clinical proof. Two-way capability (finding both adds and deletes) is now a baseline requirement, not a differentiator.

Yes. RAAPID’s AI platform can audit your current medical coding for accuracy gaps, unsupported diagnoses, and documentation deficiencies. Many organizations use our coding accuracy assessment as a first step, whether they want to benchmark an in-house team or validate the work of an outsourced coding vendor. The assessment delivers a clear report on where gaps exist and what needs to change.