执业护士
医疗保健AI暴露度
- 数据来源: BLS发布时间: 2026-08
高· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: 2026-03
0.094
0.000此处所载数值的范围0.745尺度 · 母数 · 来源
- 数据来源: ILO发布时间: 2025
0.25
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
ISCO-08职业小类单位 — 共用同一代码的职业取值相同
本站计算，并非国际劳工组织发布的数值。在本站与 ILO 数据集相连的 1,012 个职业中，达到或高于此值的占 77%。
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
Task-level exposure
显示隐藏的 18 项工作| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances.29-1171 | 0.029021.2 | 0.003210.7 |
Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses.29-1171 | 0.028220.7 | 0.008227.6 |
Provide patients or caregivers with assistance in locating health care resources.29-1171 | 0.025018.3 | 0.010936.6 |
Counsel patients about drug regimens and possible side effects or interactions with other substances such as food supplements, over-the-counter (OTC) medications, or herbal remedies.29-1171 | 0.024317.8 | 0.00289.3 |
Provide patients with information needed to promote health, reduce risk factors, or prevent disease or disability.29-1171 | 0.01279.3 | —0 |
Develop treatment plans based on scientific rationale, standards of care, and professional practice guidelines.29-1171 | 0.00916.7 | 0.00289.3 |
Prescribe medication dosages, routes, and frequencies based on patient characteristics such as age and gender.29-1171 | 0.00433.2 | —0 |
Prescribe medications based on efficacy, safety, and cost as legally authorized.29-1171 | 0.00201.5 | —0 |
Keep abreast of regulatory processes and payer systems such as Medicare, Medicaid, managed care, and private sources.29-1171 | 0.00191.4 | 0.00206.6 |
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 |
|---|---|
Maintain complete and detailed records of patients' health care plans and prognoses.O*NET Task ID 18399 | 1.0 |
Educate patients about self-management of acute or chronic illnesses, tailoring instructions to patients' individual circumstances.O*NET Task ID 18378 | 0.5 |
Schedule follow-up visits to monitor patients or evaluate health or illness care.O*NET Task ID 18379 | 0.5 |
Counsel patients about drug regimens and possible side effects or interactions with other substances, such as food supplements, over-the-counter (OTC) medications, or herbal remedies.O*NET Task ID 18380 | 0.5 |
Order, perform, or interpret the results of diagnostic tests, such as complete blood counts (CBCs), electrocardiograms (EKGs), and radiographs (x-rays).O*NET Task ID 18381 | 0.5 |
Analyze and interpret patients' histories, symptoms, physical findings, or diagnostic information to develop appropriate diagnoses.O*NET Task ID 18382 | 0.5 |
Diagnose or treat acute health care problems, such as illnesses, infections, or injuries.O*NET Task ID 18383 | 0.5 |
Diagnose or treat chronic health care problems, such as high blood pressure and diabetes.O*NET Task ID 18384 | 0.5 |
Diagnose or treat complex, unstable, comorbid, episodic, or emergency conditions in collaboration with other health care providers as necessary.O*NET Task ID 18385 | 0.5 |
Treat or refer patients for primary care conditions, such as headaches, hypertension, urinary tract infections, upper respiratory infections, and dermatological conditions.O*NET Task ID 18386 | 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