Reduce coding time, improve accuracy, and keep clinicians focused on patients.
Automate medical coding with AI that understands clinical context
MedBERT with Labelwise Attention assigns one attention head per ICD-10 code, highlighting the exact phrases that triggered each suggestion.
An inverted RAG pipeline normalizes clinical language, then retrieves the best-matching ICD-10 codes from a medical vector database via similarity search and re-ranking.
A large language model reasons across the full discharge summary — integrating lab values, procedures, and history — to generate codes with supporting rationale in one pass.
All three subsystems are combined into a calibrated weighted score. Each final code carries a confidence signal that routes high-certainty cases to auto-approval and uncertain ones to human review.
Coders review flagged cases with attention highlights, LLM rationale, and confidence scores — approving, correcting, or rejecting each suggestion. All actions feed back into model improvement.
Hospitals face rising coding workloads and staffing gaps. Our ICD solution pairs LLMs with domain rules so you ship faster, reduce denials, and keep compliance intact.
Cut coding turnaround times by automating first-pass suggestions. Improve accuracy with structured validation against ICD catalogs. Keep auditors happy with traceable prompts and rationale.
Request a DemoClinical notes, discharge summaries, and lab findings.
LLM suggests ICD codes with confidence, rationale, and highlights.
Rule checks against ICD versions, modifiers, and payer rules.
Coders approve or adjust with full audit trails.
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