Especialistas en Registros Médicos
SaludExposición a la IA
- Fuente de datos: BLSPublicado: '26.08
Muy alto· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: '26.03
0.667
0.000Rango de los valores aquí recogidos0.745Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
- Fuente de datos: ILOPublicado: '25
0.52
0.09Rango de los valores aquí recogidos0.70Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición a la IA generativa, 0–1 tal como se publica
Grupo primario de la CIUO-08 — todas las ocupaciones con ese código reciben este valor
Calculado por este sitio, no publicado por la OIT: de las 1012 ocupaciones que este sitio vincula al conjunto de datos de la OIT, un 15% alcanza o supera este valor.
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Task-level exposure
Solo tareas expuestas| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Transcribe medical reports.29-2071 | 0.009821.0 | 0.016512.7 |
Compile and maintain patients' medical records to document condition and treatment and to provide data for research or cost control and care improvement efforts.29-2071 | 0.008919.1 | 0.015311.8 |
Review records for completeness, accuracy, and compliance with regulations.29-2071 | 0.006614.1 | 0.036228.0 |
Identify, compile, abstract, and code patient data, using standard classification systems.29-2071 | 0.006012.8 | 0.030323.4 |
Retrieve patient medical records for physicians, technicians, or other medical personnel.29-2071 | 0.00439.2 | 0.013710.6 |
Process and prepare business or government forms.29-2071 | 0.00439.2 | 0.00836.4 |
Prepare statistical reports, narrative reports, or graphic presentations of information, such as tumor registry data for use by hospital staff, researchers, or other users.29-2071 | 0.00347.3 | 0.00262.0 |
Train medical records staff.29-2071 | 0.00183.9 | —0 |
Plan, develop, maintain, or operate a variety of health record indexes or storage and retrieval systems to collect, classify, store, or analyze information.29-2071 | 0.00163.4 | 0.00262.0 |
Enter data, such as demographic characteristics, history and extent of disease, diagnostic procedures, or treatment into computer. | —0 | 0.00403.1 |
| Not observed on any surface — 10 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Protect the security of medical records to ensure that confidentiality is maintained. | —0 | —0 |
Process patient admission or discharge documents. | —0 | —0 |
Release information to persons or agencies according to regulations. | —0 | —0 |
Manage the department or supervise clerical workers, directing or controlling activities of personnel in the medical records department. | —0 | —0 |
Resolve or clarify codes or diagnoses with conflicting, missing, or unclear information by consulting with doctors or others or by participating in the coding team's regular meetings. | —0 | —0 |
Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software. | —0 | —0 |
Post medical insurance billings. | —0 | —0 |
Consult classification manuals to locate information about disease processes. | —0 | —0 |
Compile medical care and census data for statistical reports on diseases treated, surgery performed, or use of hospital beds. | —0 | —0 |
Develop in-service educational materials. | —0 | —0 |
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
Occupation information
Cambios recientes que afectan a esta ocupación
abr 2026: ATE 0.36 by 2026 in SF Bay Tier 1 — the earliest crossover within healthcare support category. By 2030, 57.9% of healthcare support occupations in Tier 1 cross moderate-risk threshold.
[Fuente: arXiv 2604.00186 (Gupta & Kumar, 2026)]mar 2026: Published blog post: highest-risk healthcare support role. Automation risk 62%, medical coding 70% automated.
[Fuente: AI Changing Work Blog]