Bio‑LLM Coding, From Free‑Text to CPT/ICD in Seconds
RevCodeMD ingests free‑flowing PHI documents (encounters, H&P, procedures, discharge summaries) and returns CPT and ICD codes in seconds. A targeted 98% coding accuracy is achieved via domain‑tuned Bio‑LLM models and a continuous auto‑correction loop.
Predictions are validated against payer and specialty rules. Any corrections from coders are automatically fed back to the Coding Engine, reinforcing the model and getting smarter with each claim.
Built on our RCM services foundation, RevCodeMD aligns coding quality with downstream KPIs—clean claim rate, first‑pass approvals, and denial avoidance—so you bill right the first time.
AI-Powered Auto-Coding
CPT & ICD in seconds
98% Accuracy
Audit-ready predictions
Continuous Learning
150,000 GPU hours weekly
Next-Gen Bio-LLM
Learns from expert feedback
RevCodeMD Capabilities
Engineered to raise coding quality and accelerate reimbursement—securely and at scale.
PHI‑Aware Ingestion
Robust de‑identification & normalization pipelines for clinical narratives and notes.
CPT/ICD Suggestions
Top‑N code candidates with confidence and rationales for coder review.
Auto‑Correction Engine
Corrections stream back to train adapters—closing the loop automatically.
Compliance Guardrails
HIPAA‑aligned handling, role‑based access, and full audit trails.
RAG to Code Sets
Retrieval over ICD‑10‑CM/PCS, CPT, HCPCS, and payer policies for grounded answers.
Pre‑Submit Edits
Payer‑specific validations raise clean claim & first‑pass rates.
FHIR/HL7 Integration
Drop‑in connectors for EMR/EHR systems and claim adjudication platforms.
Coder‑in‑the‑Loop
Human oversight, explainability, and acceptance thresholds per specialty.
Bio‑LLM Architecture & Training
Purpose‑built for medical coding with domain adapters and continuous evaluation.
Model & Tokens
- Parameter targets: configurable 8B–12B trainable parameters for on‑prem latency; 20B–70B+ managed‑service option.
- Context length: 8k–32k tokens for long clinical narratives.
- Tokenizer: medical subword merges; ICD/CPT symbols kept intact for exact code spans.
- Adapters: LoRA/QLoRA domain heads (≈50–200M trainable) per specialty/payer.
Training & Ops
- Compute: ~150,000 NVIDIA H100 GPU hours / week for continuous fine‑tuning and eval.
- Precision: bfloat16 mixed precision; ZeRO/FSDP sharding; gradient checkpointing.
- RAG: retrieval over ICD/CPT knowledge, payer bulletins, and policy PDFs.
- RLHF: coder feedback loops optimize correctness and explainability.
- Metrics: Top‑1/Top‑3 code accuracy, Exact‑Match %, F1 on code spans, Claim Acceptance.
Outcomes You Can Measure
From coding speed to downstream reimbursement, RevCodeMD is built to move the metrics that matter.
For Coding Teams
- Seconds‑to‑codes with confidence & rationales.
- Higher first‑pass approvals; fewer reworks.
- Lower backlog via prioritized worklists.
For Revenue Leaders
- Improved clean claim rate and accelerated cash flow.
- Denial prevention with payer‑aware validations.
- Audit‑ready logs, role‑based access, and PHI safeguards.
Ready to See RevCodeMD?
Upload a sample note and get CPT/ICD suggestions with confidence scores.