Healthcare Practitioners and Technical Workers, All Other
AI touches this job. It does not define it.
AI meaningfully touches parts of this work, yet the majority of its activities remain outside observed AI usage. That's balanced exposure: real efficiency gains are available without wholesale overlap.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
All healthcare practitioners and technical workers not listed separately.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Administer basic health care or medical treatments. | 15.4% | Not observed 0.00 |
| Analyze health or medical data. | 11.0% | Moderate 0.49 |
| Assist others to access additional services or resources. | 9.6% | High overlap 0.61 |
| Evaluate patient or client condition or treatment options. | 9.3% | Not observed 0.00 |
| Advise patients or clients on medical issues. | 7.9% | High overlap 0.66 |
| Explain medical information to patients or family members. | 7.8% | High overlap 0.83 |
| Examine people or animals to assess health conditions or physical characteristics. | 7.8% | Not observed 0.00 |
| Diagnose health conditions or disorders. | 7.8% | Not observed 0.00 |
| Monitor health conditions of humans or animals. | 7.0% | Not observed 0.00 |
| Develop patient or client care or treatment plans. | 6.0% | Not observed 0.00 |
| Administer diagnostic tests to assess patient health. | 5.4% | Not observed 0.00 |
| Collect environmental or biological samples. | 4.9% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is analyze health or medical data; the most important activity AI is not observed doing is administer basic health care or medical treatments. The plan below is built around exactly that split.
Balanced exposure: take the gains, deepen the moat.
Next 30 days
- Harvest the easy wins. Parts of this role, led by Analyze health or medical data, are already AI-assistable. Claim those hours back this month.
- Map your own mix. Your split may differ from the occupation average. Run the six-question personalization above and read your range, not the headline.
Next 90 days
- Invest saved time in the human core. The majority of this occupation's activities show no observed AI usage. Deepen exactly those, starting with Administer basic health care or medical treatments.
- Stay current, lightly. A monthly hour trying new tools on your real tasks beats panic-learning later.
The longer game
- Own a niche. Moderate exposure means AI will not define this occupation, but specialists who pair domain depth with tool fluency will out-earn generalists in it.
- Watch the boundary activities. If the 'Emerging' rows in your table above turn 'High' in a future data update, revisit the plan. No countdown clocks, just recheck.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.