Compatible with any HL7- or FHIR-capable EMR
Platform Architecture

Six models. One verdict.

A six-stage orchestration of ML models and frontier LLMs — reading the note, checking payer rules, and routing by confidence at the point of care.

→ Inbound HL7 ADT · ORU · MDM
Integration Mirth Connect · FHIR
Edge WAFv2 · TLS/ACM · VPC
NLP Parser
Stream tokenizer · medical NER
ICD-10 Model
XGBoost · 72k labels
CPT Model
LightGBM · modifier-aware
Consensus Layer
Frontier LLM voting
Payer Rulebook
CMS + commercial rules
Confidence Scorer
Auto-post vs. review
← Outbound DFT Charges · ORU Reports
Storage Encrypted Data Lake · SSE-AES256
Writeback Self-learning training loop

How it works.

A CMS-trained ensemble — NLP extraction, gradient-boosted classifiers, and a payer rulebook in a single inference pipeline with confidence-threshold routing.

1

Note Generated

Clinician completes the visit note in the EHR or via voice dictation.

2

AI Processes

CodeSightTM ensemble parses the clinical data and applies payer rules in real time.

3

Codes Assigned

ICD-10, CPT, and modifiers posted with confidence scoring and a review queue.

4

Revenue Posted

Charges flow to your billing queue. DFT sent to the payer via HL7 / X12.

Why it wins.

Recover Lost Revenue

Clinically defensible codes mean fewer denials. Correct modifiers prevent underpayment. Automated charge capture eliminates missed charges.

Reduce Charge Lag

Real-time HL7 triggers eliminate queuing time. Codes assigned in seconds, not days. Charges hit the billing queue immediately after visit close.

Lower Audit Risk

Confidence scoring creates audit trails. Every code is defensible. Payer rules prevent coding errors that trigger audits.

A short look at the engine.

Conceptual overview — for an actual product walkthrough, start a free pilot.

Two clinicians reviewing the Medmio app — Supercharge Your Practice with AI Medical Coding Concept video

Hosted on YouTube. Click to play — no autoplay.

Frequently Asked Questions

Accuracy varies by specialty and clinical complexity, but CodeSightTM’s ensemble approach consistently outperforms single-model solutions and outperforms manual human coders in both speed and accuracy. We provide confidence scores so you can review low-confidence codes before billing.

CodeSightTM integrates via HL7v2 and FHIR REST APIs, which are supported by virtually all modern EHRs. We have pre-built integrations for Athenahealth, eClinicalWorks, Epic, Cerner, and others. Contact us for a compatibility check with your specific EHR.

Implementation typically takes 2-6 weeks from contract to production, depending on your EHR and integration complexity. We handle all HL7 mapping, testing, and staff training. Your team stays involved throughout, and we provide 24/7 support during go-live.

Yes. CodeSightTM is HIPAA-compliant and meets HITECH requirements. All data is encrypted in transit (TLS 1.2+) and at rest (256-bit AES). We undergo regular security audits and penetration testing. Data is processed in AWS with BAA in place. We never use your data for model training.

Yes. You control the confidence threshold for auto-billing. Codes below your threshold are flagged for manual review before posting. Most practices set thresholds at 95%+, but you can adjust based on your risk tolerance and audit history.

CodeSightTM's coding accuracy and payer rules dramatically reduce denials. When denials do occur, our RCM Analytics module tracks denial codes and reasons, helping you spot systemic issues (like bundling violations or missing modifiers) for quick remediation.

See CodeSightTM in Action

Watch how clinical notes become accurate, compliant charges in real-time.