Automation index
How much of a ICU nurse’s job can AI already do?
A vitals log comes earlier. Catching the change and calling in time do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
ICU nurse
20%
AI can already do 20% of this job.
#452 / 633
View rankingICU nurse
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 |
|---|---|---|---|
Charts, pumps and the bed plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Lines, ventilator and the patient who crashes.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Hygiene, turning and preventing sores.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Family at the bed and the news.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
On-call, admissions and handover.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Catch the change and owning the bed.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- ICU care
- run the ventilator
- central lines
- chart vitals
- prevent sores
- ICU handover
- infusion pumps
- call the intensivist
Sources for this job· O*NET 29-1141.00· ESCO/ISCO 2221
IS WORK OVER?
ML engineer
29%
#64 / 633
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