AI exposure: Phlebotomists

At 12.2% of weighted task load, Phlebotomists sits at the 19th percentile, below the point where a job's centre of gravity has moved. 72.5% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.

What holds the line here is verification cost. Across this occupation's 23 tasks it averages 2.33 out of 3, the highest of the five friction dimensions. In plain terms, checking the output costs more than producing it. Where an undetected error is expensive, dangerous or irreversible, the economics change. Someone has to verify the work, and verifying can cost as much as doing it. This is the friction most likely to fall as tools for checking improve.

The most exposed thing this job does is Enter patient, specimen, insurance, or billing information into computer, at 60.0%. The least is Calibrate or maintain machines, at 0.0%. A gap of 60.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within healthcare support occupations, this one is more exposed than most. The median across the 19 roles in the group is 11.4%, and only 7 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.

What would move this score. Of 23 tasks, 2 are currently banded exposed, 4 assisted and 17 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.