LO
Medical assistant / clinic assistant — 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.
The steepest curve in the health group, and all of it comes from one half of the job. The administrative half — authorisations, claims, scheduling, insurance correspondence — is a rules-based document workflow where the payer returns an approval or a coded denial, so a system can retry unattended; that is the profile automation needs and it is why this climbs while the doctor's curve stays flat. It flattens from 2025 because the remaining tasks are the clinical ones: injections, dressings, swabs, and the telephone judgement about who needs to be seen today. Read the height as paperwork, and note what the curve cannot show — both sides of the authorisation process are automating, so fewer human hours in the loop does not mean fewer loops.
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
- 2026-09-14
- Basis of the task judgements
- 2 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 1