IS WORK OVER?

Automation index

How much of a Oncologist’s job can AI already do?

A tumour board and a protocol come earlier. Saying a prognosis and holding the treatment do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Oncologist

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.

TaskShareAIPoints
Read pathology, imaging and regimen protocols.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Clinic, bad news and ceiling-of-care decisions.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Prescribe, watch toxicity and adjust cycles.
Estimate

No public source provides a precise figure. This is our estimate.

18%12%+2.2
Reports, consents and tumour board.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Coordinate surgery, radio and palliative care.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
On-call, team and not abandoning the patient.
Estimate

No public source provides a precise figure. This is our estimate.

16%16%+2.6
Total100%22%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • Reports, consents and tumour board
  • oncologo
  • oncologist

Sources for this job· O*NET 29-1229.00· ESCO/ISCO 2212

Last-mile manager

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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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