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

Exposed tasks only
TaskClaude.aiRaw / share %APIRaw / share %
Schedule medical appointments for patients.

29-2072

0.00000.00.010033.3
Consult classification manuals to locate information about disease processes.

29-2072

0.00000.00
Identify, compile, abstract, and code patient data, using standard classification systems.
00.020066.7
Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software.
00.00000.0
Enter data, such as demographic characteristics, history and extent of disease, diagnostic procedures, or treatment into computer.
00.00000.0
Not observed on any surface — 12 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.
Compile and maintain patients' medical records to document condition and treatment and to provide data for research or cost control and care improvement efforts.
00
Maintain or operate a variety of health record indexes or storage and retrieval systems to collect, classify, store, or analyze information.
00
Post medical insurance billings.
00
Process and prepare business or government forms.
00
Process patient admission or discharge documents.
00
Protect the security of medical records to ensure that confidentiality is maintained.
00
Release information to persons or agencies according to regulations.
00
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.
00
Retrieve patient medical records for physicians, technicians, or other medical personnel.
00
Review records for completeness, accuracy, and compliance with regulations.
00
Scan patients' health records into electronic formats.
00
Transcribe medical reports.
00

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]