LO
Medical coder — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 1 verified event for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts at 36 because computer-assisted coding — software that reads a record and suggests codes — was already in hospitals well before this chart begins. It climbs steadily as language models make the suggestions better on long, messy records and as some health systems begin sending routine charts the software is confident about straight to billing. It stops short of the top because complex inpatient cases, queries to doctors, audits and appeals still need a coder who knows the rules, and because someone has to answer for what the software codes.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-09-26
- Basis of the task judgements
- 0 evidence-backed · 6 platform inference · 0 not enough evidence
- Verified events
- 1