Automation index
How much of a Medical secretary’s job can AI already do?
A clinic diary comes earlier. The patient going in and the record being there do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Medical secretary
27%
AI can already do 27% of this job.
#177 / 623
View rankingMedical secretary
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 |
|---|---|---|---|
Diary, appointments and the DNA.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Record, the report and the missing one.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Phone, results and not giving them wrong.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The doctor, the delay and not wrecking the waiting room.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Confidentiality and not talking in the corridor.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
The patient goes in, and the record is there.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- clinic diary
- records
- results
- confidentiality
- waiting room
- medical appointments
- reports
- DNAs
Sources for this job· O*NET 43-6013.00· ESCO/ISCO 3344
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