Automation index
How much of a Phlebotomist’s job can AI already do?
A queue and a label come earlier. Finding the vein and not sticking twice do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Phlebotomist
18%
AI can already do 18% of this job.
#574 / 643
View rankingPhlebotomist
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Bookings, labels and tubes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Puncture, draw and not haemolyse.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Calm children, fear and faints.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Spin, log and send to the lab.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Waste, needlesticks and hygiene.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Not mixing up a patient or a tube.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Bookings, labels and tubes
- Puncture, draw and not haemolyse
- Calm children, fear and faints
- Spin, log and send to the lab
- Waste, needlesticks and hygiene
- Not mixing up a patient or a tube
- flebotomista
- extractora
- phlebotomist
Sources for this job· O*NET 31-9097.00· ESCO/ISCO 5329
IS WORK OVER?
ML engineer
29%
#64 / 643
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511