Accuracy is not a marketing claim, it is a measurable distribution. We open the hood on how leading autonomous platforms achieve consistent accuracy above 99 percent, and where the remaining 1 percent comes from.
01 / WHATWhat accuracy actually means
The number you read is rarely the number you get.
When a vendor says 99 percent accuracy, ask three questions. Accuracy on what chart types. Measured against what reference set. Including or excluding edge cases. The honest answer is a distribution, not a number.
02 / STACKInside the four layer QA stack
Accuracy is engineered, not declared.
03 / ERRORSWhere remaining errors come from
The 1 percent worth understanding.
Documentation gaps
If the note is wrong, the code is wrong. Garbage in, garbage out.
Genuine ambiguity
Some clinical scenarios are coder judgment calls. AI flags, human decides.
Rule lag
Payer rule changes that have not yet propagated to the engine.
04 / AUDITHow to audit your own coding
Run this every quarter.
- Pull a random 250 chart sample, weighted by chart type mix
- Have a credentialed coder code them blind
- Compare to the AI output
- Categorize disagreements by root cause
- Feed the disagreement set back into model retraining
If you are not running quarterly blind audits on your AI, you are not running AI. You are hoping.
05 / ROADMAPReaching 99 percent in 90 days
If you are below it today.
Above 99 percent accuracy is engineered, not gifted. The four layer stack plus quarterly retuning is what separates platforms that hold accuracy from platforms that drift.