Medical Records Specialists

Healthcare

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-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

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.667

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.52

    0.09Range of values carried here0.70

    Group-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.

    Source dataset

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

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.

TaskβE1 + 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®, 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]