Automation index
How much of a Hospital porter’s job can AI already do?
A transfer ping comes earlier. Moving someone on drips and not dropping them does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Hospital porter
16%
AI can already do 16% of this job.
#611 / 643
View rankingHospital porter
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 |
|---|---|---|---|
Alerts, destinations and transfer notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Trolleys, chairs and moving patients.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Theatre, mortuary and hospital stores.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Dealing with families, discharges and waits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Support nursing and bay safety.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
Shifts, physical load and not hurting your back.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 16% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Alerts, destinations and transfer notes
- Trolleys, chairs and moving patients
- Theatre, mortuary and hospital stores
- Support nursing and bay safety
- celador
- camillero
- hospital porter
Sources for this job· O*NET 31-1132.00· 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