Automation index
How much of a Paramedic’s job can AI already do?
Paramedics treat emergencies outside hospital and move the patient.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Paramedic
14%
AI can already do 14% of this job.
#640 / 643
View rankingParamedic
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 |
|---|---|---|---|
Notes and protocolsEstimateNo public source provides a precise figure. This is our estimate. | 20% | 30% | +6 |
Street care and transferEstimateNo public source provides a precise figure. This is our estimate. | 50% | 8% | +4 |
Life-threatening callsEstimateNo public source provides a precise figure. This is our estimate. | 20% | 12% | +2.4 |
CoordinationEstimateNo public source provides a precise figure. This is our estimate. | 10% | 15% | +1.5 |
| Total | 100% | 14% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- hygiene in a health care setting
- behavioural science
- pharmacology
- disorders of vital functions
- sanitary technology
- transportation methods
- pathogenic microorganisms
- sociology applied to paramedical science
- health care system
- human anatomy
Sources for this job· O*NET 29-2042.00· ESCO/ISCO 3258
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