CAC vs autonomous coding: what's the difference?

Computer-assisted coding (CAC)

CAC is the older of the two. Software — historically driven by natural language processing and rules — scans documentation and suggests codes; a human coder then confirms, edits, or rejects each one. CAC speeds up review but still depends on a coder for every chart, and the suggestions often arrive without an explicit, traceable reason.

Autonomous coding

Autonomous coding aims to remove the human from a portion of the work entirely: the system assigns codes and sends the charts it's confident about straight to billing, escalating the rest to human coders. It can process high volumes quickly. The catch is defensibility — on the charts it auto-codes, the reasoning is often a black box, which is exactly what undermines confidence in an audit.

Side by side

The axis both leave out: transparency & measurement

Framing the choice as “assist vs automate” misses what matters most to a compliance-minded buyer: can you see and verify each code, and is accuracy measured honestly? Compliance experts widely note that CMS, the DOJ, and the OIG expect strong human oversight, audit trails, and documentation support — and the DOJ has pursued cases involving automated up-coding. A system can be highly automated and fully transparent; those are different axes.

Where Capsa sits

Capsa is transparent, measured decision support. It keeps coders in control — like CAC — but answers the questions autonomous black boxes can't: every recommended code links to the human-readable rule that produced it and the verbatim chart text that triggered it, at the exact version that ran, and accuracy is reported as scope-aware precision and recall against what your coders actually billed. It's built to partner with coders, not replace them, and it runs on your data with no model lock-in.

Frequently asked questions