Automation index
How much of a Blood bank technician’s job can AI already do?
An automated crossmatch comes earlier. Confirming the group and not issuing the wrong bag do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Blood bank technician
28%
AI can already do 28% of this job.
#120 / 623
View rankingBlood bank technician
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 |
|---|---|---|---|
Group, screen and the bag crossmatch.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Fractionate, label and traceability.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The emergency, the massive and the ID error.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Donation, apheresis and the donor who feels unwell.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Stock, expiry and what theatre asks for.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The right bag and owning the blood group.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- crossmatch blood
- blood group
- fractionate products
- donor apheresis
- bag traceability
- massive transfusion
- label units
- blood bank
Sources for this job· O*NET 29-2011.00· ESCO/ISCO 3212
IS WORK OVER?
ML engineer
29%
#64 / 623
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