Automation index
How much of a Health information technician’s job can AI already do?
An ICD-10 and a file come earlier. Coding it right and having the record when it is needed do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Health information technician
31%
AI can already do 31% of this job.
#43 / 623
View rankingHealth information 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 |
|---|---|---|---|
Code ICD, the discharge set and the episode.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Filing, scanning and the missing record.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Loans, copies and the judge who wants the file.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Documentation emergencies and the discharge that will not close.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Data quality, audits and billing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Confidentiality and not losing the record.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 31% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- code ICD-10
- discharge dataset
- file records
- loan records
- health documentation
- scan reports
- clinical data quality
- confidentiality
Sources for this job· O*NET 29-2072.00· ESCO/ISCO 3252
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