Medical Records Specialists
HealthcareAI exposure
- Data source: BLSPublished: '26.08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
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: AnthropicPublished: '26.03
0.667
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: '25
0.52
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 15% score at or above this value.
What kind of figure this source publishes
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.
Task-level exposure
Exposed tasks only| Task | Claude.aiRaw / share % |
|---|---|
Review records for completeness, accuracy, and compliance with regulations.29-2071 | 0.022423.3 |
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 | 0.015816.4 |
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 | 0.00899.2 |
Identify, compile, abstract, and code patient data, using standard classification systems.29-2071 | 0.00869.0 |
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 | 0.00778.0 |
Develop in-service educational materials.29-2071 | 0.00666.9 |
Transcribe medical reports.29-2071 | 0.00636.5 |
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.29-2071 | 0.00464.7 |
Consult classification manuals to locate information about disease processes.29-2071 | 0.00404.2 |
Compile medical care and census data for statistical reports on diseases treated, surgery performed, or use of hospital beds.29-2071 | 0.00383.9 |
| Not observed on any surface — 7 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 |
Retrieve patient medical records for physicians, technicians, or other medical personnel. | —0 |
Process patient admission or discharge documents. | —0 |
Manage the department or supervise clerical workers, directing or controlling activities of personnel in the medical records department. | —0 |
Train medical records staff. | —0 |
Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software. | —0 |
Post medical insurance billings. | —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
Recent Changes Affecting This Occupation
Apr 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.
[Source: arXiv 2604.00186 (Gupta & Kumar, 2026)]Mar 2026: Published blog post: highest-risk healthcare support role. Automation risk 62%, medical coding 70% automated.
[Source: AI Changing Work Blog]