AI-Powered Medical Coding That Gets It Right
AI assistant for healthcare providers that reads clinical notes, suggests ICD-10 codes, calculates HCC risk scores, and ensures audit-ready documentation. Reduce coding errors, capture missing diagnoses, and optimize revenue — all with AI precision.
No manual code lookup. No missed diagnoses. Just accuracy.
Import physician notes, discharge summaries, or H&P records in any format.
NLP engine identifies diagnoses, procedures, and clinical indicators from unstructured text.
Automatically maps conditions to ICD-10-CM codes and calculates HCC risk adjustment factors.
Review suggested codes with confidence scores, approve, and export audit-ready documentation.
Everything healthcare providers need for accurate, efficient, and compliant medical coding.
Advanced natural language processing reads physician notes, discharge summaries, and H&P records. Understands medical terminology, abbreviations, and context.
Searches across 72,000+ ICD-10-CM codes to find the most specific, accurate code. Suggests primary and secondary diagnoses with confidence percentages.
Calculates RAF scores across 86 HCC categories. Identifies missed diagnoses that affect risk scores and reimbursement accuracy.
Every code suggestion is linked to the supporting clinical evidence. Full audit trail with timestamps, confidence scores, and source text highlights.
Built-in compliance rules for CMS, OIG, and payer-specific guidelines. Flags potential upcoding, unbundling, and documentation gaps before submission.
Identifies missed diagnoses and under-coded conditions. Captures legitimate revenue by ensuring clinical documentation supports the highest specificity codes.
Built with healthcare-grade technology
ICD-10 Codes
HCC Categories
Accuracy Target
Faster Coding
End-to-end automated coding from clinical notes to revenue reports.
graph LR
A["Clinical Notes"] --> B["NLP Parser"]
B --> C["Condition Extractor"]
C --> D["ICD-10 Matcher"]
D --> E["HCC Calculator"]
E --> F["Audit Trail"]
F --> G["Revenue Report"]
style A fill:#fee2e2,stroke:#dc2626,color:#991b1b
style B fill:#fef3c7,stroke:#d97706,color:#92400e
style C fill:#dbeafe,stroke:#2563eb,color:#1e40af
style D fill:#ede9fe,stroke:#7c3aed,color:#5b21b6
style E fill:#d1fae5,stroke:#059669,color:#065f46
style F fill:#f3e8ff,stroke:#9333ea,color:#6b21a8
style G fill:#dcfce7,stroke:#16a34a,color:#166534
See clinical notes, suggested codes, and audit trail side by side.
Patient presents with Type 2 diabetes mellitus with diabetic chronic kidney disease, stage 3.
History of congestive heart failure, currently stable on medication...
H&P - Dr. Smith - 03/15/2026
Built for modern healthcare, not legacy workflows.
| Feature | HCC Medical Coding | 3M CodeFinder | Optum EncoderPro | Manual Coding |
|---|---|---|---|---|
| AI-powered NLP parsing | Yes | Limited | No | No |
| HCC risk score calculation | Automatic | Separate tool | Add-on | Manual |
| Audit trail | Built-in | Basic | Basic | Paper-based |
| Pricing | Affordable SaaS | $25K+/year | $$$ | Staff costs |
| Setup time | Minutes | Weeks | Weeks | Months |
| Coding speed | 50% faster | 20% faster | 15% faster | Baseline |
AI suggests codes in real-time as physicians document encounters. No post-visit coding delay.
Custom rule engines for Medicare, Medicaid, and commercial payers. Maximize reimbursement per payer.
Clinical Documentation Improvement queries generated automatically. Close documentation gaps before billing.
A complete AI coding platform for healthcare revenue cycle management.
Clinical note parsing, condition extraction, and ICD-10-CM code suggestion with confidence scoring. Core coding workflow.
RAF score calculation, HCC category mapping, missed diagnosis detection, and risk adjustment factor optimization.
Live coding during encounters, CDI query generation, documentation gap alerts, and physician collaboration tools.
Payer-specific rule engines, claims pre-submission validation, denial prevention, and direct EHR integration (Epic, Cerner, athenahealth).
Everything you need to know about HCC Medical Coding.
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