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Automation risk assessment

Medical and Health Services Managers: AI exposure and automation risk

Medical and Health Services Managers score 37% on the Eloundou et al. LLM exposure measure (moderate exposure). 11 of 18 rated tasks are exposed. BLS projects +24.2% employment, 2025–35.

Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026

LLM exposure (β)

37%

Moderate exposure

BLS projects +24.2% employment for medical and health services managers from 2025 to 2035.

Eloundou et al., Science 2024

Exposure scores

How much of this occupation's task list large language models could speed up

37%

Moderate exposure (β, human-rated)

Share of this occupation's tasks that annotators judged an LLM could do at least 50% faster at the same quality, counting tasks that need extra software at half weight.

Eloundou et al., Science 2024

43%

Same measure, rated by GPT-4

The study's model-rated β for comparison. Directly exposed tasks alone (α) score 11%; all exposed tasks (ζ) score 63%.

Eloundou et al., Science 2024

7%

Observed exposure in real AI usage

How much of the theoretically exposed work actually shows up as automated, work-related Claude usage.

Anthropic observed exposure (Mar 2026)

Exposed and unexposed tasks

Human ratings of 18 O*NET task statements, Eloundou et al., Science 2024

Of 18 rated tasks, 2 are directly exposed (E1), 9 are exposed once complementary software is built (E2), and 7 are not exposed (E0).

Exposed tasks

  • Develop and maintain computerized record management systems to store and process data, such as personnel activities and information, and to produce reports.

    E2 · Exposed with added software

  • Plan, implement, and administer programs and services in a health care or medical facility, including personnel administration, training, and coordination of medical, nursing and physical plant staff.

    E2 · Exposed with added software

  • Maintain awareness of advances in medicine, computerized diagnostic and treatment equipment, data processing technology, government regulations, health insurance changes, and financing options.

    E2 · Exposed with added software

  • Establish work schedules and assignments for staff, according to workload, space, and equipment availability.

    E2 · Exposed with added software

  • Manage change in integrated health care delivery systems, such as work restructuring, technological innovations, and shifts in the focus of care.

    E2 · Exposed with added software

Tasks not exposed

  • Direct, supervise and evaluate work activities of medical, nursing, technical, clerical, service, maintenance, and other personnel.

    E0 · Not exposed

  • Conduct and administer fiscal operations, including accounting, planning budgets, authorizing expenditures, establishing rates for services, and coordinating financial reporting.

    E0 · Not exposed

  • Monitor the use of diagnostic services, inpatient beds, facilities, and staff to ensure effective use of resources and assess the need for additional staff, equipment, and services.

    E0 · Not exposed

  • Direct or conduct recruitment, hiring, and training of personnel.

    E0 · Not exposed

  • Maintain communication between governing boards, medical staff, and department heads by attending board meetings and coordinating interdepartmental functioning.

    E0 · Not exposed

Job outlook

U.S. projections, BLS Employment Projections 2025–35

+24.2%

Projected employment change, 2025–35

From 640,400 jobs in 2025 to 795,500 in 2035. All occupations: +3.5%.

BLS Employment Projections 2025–35

62,300

Openings per year, 2025–35 average

Includes openings from growth and from workers who retire or change occupations.

BLS Employment Projections 2025–35

$123,860

Median annual wage, 2025

BLS Employment Projections 2025–35

Bachelor's degree

Typical education for entry

BLS Employment Projections 2025–35

What BLS expects to change

Demand change - share increases as the growing complexity of healthcare delivery increases the demand for medical and health services managers.

BLS note on factors affecting medical and health services managers employment, Table 1.12, BLS Employment Projections 2025–35.

Primary profiles: BLS Occupational Outlook Handbook · O*NET OnLine

How AI is used on this work

Claude.ai conversations on this occupation's tasks, Anthropic Economic Index (May 2026)

Augmentation (AI works with the person)36.9%
Automation (AI does the task)63.1%

Augmentation and automation are shares of conversations with a classified collaboration pattern, excluding unclassified conversations. The individual pattern shares include them. 90.4% of all conversations on these tasks were for work. See the full collaboration breakdown

Live openings

Current listings in Metaintro's jobs index

Browse current healthcare administrator openings in Metaintro's jobs index.

See healthcare administrator jobs by city

Sources & methodology

Every figure on this page comes from the public datasets below. We do not edit the source values; derived figures are explained here.

  1. Exposure is the human-annotator β score from Eloundou et al.: the share of an occupation's O*NET tasks that a large language model could do at least 50% faster at equal quality, counting tasks that need extra software (E2) at half weight. We label β under 0.25 low, 0.25–0.5 moderate, and 0.5 or more high; these bands are ours, not the authors'.
  2. Task labels (E0 not exposed, E1 directly exposed, E2 exposed with complementary software) are the study's aggregated human ratings of each O*NET task statement for the occupation. We show up to five tasks per group.
  3. Observed exposure (Anthropic, March 2026) combines that theoretical feasibility with real Claude usage on the occupation's tasks, weighting automated and work-related use more heavily than augmentative use, then averaging by time spent on each task. Values run from 0 to 1.
  4. Employment, growth, openings, wage, and education are the BLS 2025–35 projections as published; BLS utilization notes are quoted verbatim.
  5. Collaboration shares come from Anthropic Economic Index data for Claude.ai conversations in May 2026, classified to the O*NET tasks they match. "Automation" groups directive and feedback-loop conversations; "augmentation" groups task iteration, learning, and validation. Both buckets are shares of conversations with a classified collaboration pattern, excluding the unclassified "none" pattern; the six individual pattern shares include unclassified conversations. They describe how people use Claude on this occupation's tasks — not what share of workers in the occupation use AI.
  6. BLS occupations are matched to O*NET-SOC codes with the BLS O*NET-to-NEM crosswalk; where a BLS occupation spans several O*NET occupations, we average their values.
  7. Exposure measures what AI could help do, not a forecast of job loss.

  • Eloundou et al., Science 2024

    Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). "GPTs are GPTs: Labor market impact potential of LLMs." Science, 384(6702), 1306–1308. doi:10.1126/science.adj0998. Occupation- and task-level exposure data from the authors' public repository (openai/GPTs-are-GPTs).

    Vintage: Published June 21, 2024; human annotations of O*NET tasks collected 2023. License: Data repository: MIT License. Retrieved 2026-10-09.

  • Anthropic observed exposure (Mar 2026)

    Massenkoff, M., & McCrory, P. (2026). "Labor market impacts of AI: A new measure and early evidence." Anthropic, March 5, 2026. Occupation-level observed exposure (Anthropic Economic Index, labor_market_impacts/job_exposure.csv).

    Vintage: Published March 5, 2026. License: CC BY (data). Retrieved 2026-10-09.

  • BLS Employment Projections 2025–35

    U.S. Bureau of Labor Statistics, Employment Projections program. Occupational projections, 2025–35, and worker characteristics, 2025 (Table 1.2); fastest growing occupations (Table 1.3); factors affecting occupational utilization (Table 1.12).

    Vintage: 2025–35 projections, released August 27, 2026. License: Public domain (U.S. federal government work). Retrieved 2026-10-09.

  • Anthropic Economic Index (May 2026)

    Massenkoff, M., Lyubich, E., Sacher, S., Hitzig, Z., Zhang, S., Heller, R., & McCrory, P. (2026). "Anthropic Economic Index report: Cadences." Anthropic, June 26, 2026. Claude.ai usage metrics by occupation, global, May 2026.

    Vintage: Release of June 26, 2026; Claude.ai conversations from May 2026. License: CC BY (data). Retrieved 2026-10-09.

  • O*NET 31.0 Database

    This page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. Metaintro has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.

    Vintage: O*NET 31.0, August 2026 release. License: CC BY 4.0. Retrieved 2026-10-09.

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