Clinical Documentation Specialists
HealthcareAI exposure
Show 15 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Schedule medical appointments for patients.29-2072 | 0.00000.0 | 0.010033.3 |
Identify, compile, abstract, and code patient data, using standard classification systems. | —0 | 0.020066.7 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software.O*NET Task ID 22877 | 1.0 |
Enter data, such as demographic characteristics, history and extent of disease, diagnostic procedures, or treatment into computer.O*NET Task ID 22880 | 1.0 |
Process and prepare business or government forms.O*NET Task ID 22884 | 1.0 |
Transcribe medical reports.O*NET Task ID 22893 | 1.0 |
Compile and maintain patients' medical records to document condition and treatment and to provide data for research or cost control and care improvement efforts.O*NET Task ID 22878 | 0.5 |
Consult classification manuals to locate information about disease processes.O*NET Task ID 22879 | 0.5 |
Identify, compile, abstract, and code patient data, using standard classification systems.O*NET Task ID 22881 | 0.5 |
Maintain or operate a variety of health record indexes or storage and retrieval systems to collect, classify, store, or analyze information.O*NET Task ID 22882 | 0.5 |
Post medical insurance billings.O*NET Task ID 22883 | 0.5 |
Process patient admission or discharge documents.O*NET Task ID 22885 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Exposure figures by source
This site carries AI exposure figures from 4 datasets. For this occupation, 4 of them publish a figure of the kind shown below; the 3 published most recently are displayed. The full list is on the Credits & Sources page.
Data source: BLS
Very high
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
Data source: Anthropic
0.667
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Data source: ILO
0.52
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Each figure is published on its own scale and measures something different, so they cannot be added, averaged, or ranked against one another.
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
BLS draws on research that is also shown here — Anthropic — so a resemblance between them is not independent confirmation but the same input read twice.
AI exposure (ILO)
0.52 / 1
top 15% of all occupations