WhiteBox runs every clinical note through multiple AI models to find the right code. When they agree, auto-code. When they disagree, route to a certified coder with the full breakdown. Every decision auditable. Every code defensible.
Clinical note says: "patient presents with chest pain, radiating to left arm, with shortness of breath." Is this R07.9 (chest pain, unspecified), I20.9 (angina pectoris, unspecified), or I21.9 (acute myocardial infarction, unspecified)?
The specificity level determines reimbursement. One model picks the generic code, another picks the specific one. WhiteBox shows the disagreement so a coder picks the correct specificity.
Encounter note mentions diabetes management AND a new foot ulcer. The primary reason for visit is diabetes, but the foot ulcer is a separate billable code.
Single model codes diabetes (E11.9) and misses the ulcer (L97.529). Multiple models catch both conditions because they interpret the note differently.
45-minute complex office visit with counseling. One model codes 99214 (moderate complexity), another codes 99215 (high complexity). The difference is $80 per visit.
Coding too high is fraud. Coding too low loses revenue. WhiteBox flags the disagreement so a coder confirms the correct level, protecting you both ways.
Every run, every log-prob, every disagreement -- recorded. Replay any decision from its ID.
Auto-code discharge summaries with multi-model consensus. Flag complex cases for certified coders. Reduce coding backlog.
Classify office visit complexity (99211-99215) accurately. Protect against upcoding audits and downcoding revenue loss.
Code ED visits in real time from provider notes. Priority routing for high-complexity cases.
Map operative notes to CPT codes. Flag when models disagree on primary vs secondary procedures.
Auto-code imaging reports. Catch specificity errors before claim submission.
Pre-check codes before submission. When models disagree on a code, review it before the payer denies it.
Every coding decision logged with model votes, confidence scores, and final assignment. Exportable for compliance audits.
No code is finalized without confidence. Low-confidence codes always route to a certified coder with the complete model breakdown.
When a payer audits a code, you can show: "4 AI models agreed on I21.3, OR 2 disagreed and a certified coder reviewed the case and selected the final code." That is defensible.
| Feature | Manual coding | Single AI model | WhiteBox |
|---|---|---|---|
| Speed | 15-20 min per encounter | Seconds | Seconds |
| Accuracy | High but slow | Unknown error rate | Measured by consensus |
| Specificity | Expert catches nuance | Often picks generic code | Flags specificity disagreements |
| Upcoding risk | Low (human judgment) | Undetected | Caught by model disagreement |
| Audit trail | Coder signature | No | Every model vote logged |
| Denial rate | 5-10% | Unknown | Reduced by pre-check |
20 free to start. No credit card.
That's 1,000 classifications for $10.
20 free classifications. Then $0.01 each. The audit trail starts the moment you install.