Coding and E/M capture: getting paid for the work you do
Under-coding leaves money on the table. How AI-assisted E/M and CPT capture help you bill accurately.
Ask a certified coder to audit a typical practice and they will usually find the same thing: not fraud, but timidity. Visits documented as moderate-complexity decision making, billed at a low-level code "to be safe." Multiplied across a year, cautious coding quietly costs practices real revenue for work they genuinely performed.
Why under-coding happens
The 2021 E/M rules moved the game to medical decision making and time, but most clinicians never got a working mental model of the new levels. Faced with ambiguity at the end of a long day, they round down. The documentation supports a 99214; the claim says 99213.
What AI-assisted capture changes
Software that reads the actual note can propose the code the documentation supports — with the reasoning shown: the problems addressed, the data reviewed, the risk discussed. The clinician or biller confirms or overrides. Three things follow:
- Accuracy in both directions. The same analysis that catches under-coding flags codes the note does not support — protection against audits, not just lost revenue.
- The note improves. When the suggestion says "this supports level 4 except the data review is undocumented," clinicians learn exactly what their notes are missing.
- Charges stop leaking. Procedures, vaccines, and point-of-care tests mentioned in the note but never charged are surfaced before the claim goes out.
The goal is not aggressive coding. It is telling the truth about the visit — completely, every time.