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Emerging role · No. 24 fastest-growing

Health Information Technologists and Medical Registrars: job outlook 2025–35

BLS projects health information technologists and medical registrars employment to grow 15.9% from 2025 to 2035 (No. 24 fastest), with 3,000 openings a year and a $68,020 median wage.

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

Projected growth, 2025–35

+15.9%

Typical entry education: Associate's degree.

BLS Employment Projections 2025–35

Job outlook

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

+15.9%

Projected employment change, 2025–35

From 42,000 jobs in 2025 to 48,600 in 2035. All occupations: +3.5%.

BLS Employment Projections 2025–35

3,000

Openings per year, 2025–35 average

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

BLS Employment Projections 2025–35

$68,020

Median annual wage, 2025

BLS Employment Projections 2025–35

Associate's degree

Typical education for entry

BLS Employment Projections 2025–35

What BLS expects to change

Demand change - share increases as larger amounts of health data are generated over the coming years. Organizations across all industries, especially those tied to healthcare, will need technologists and registrars to process the data to support operational and clinical decision-making.

BLS note on factors affecting health information technologists and medical registrars employment, Table 1.12, BLS Employment Projections 2025–35.

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

Core tasks

Highest-importance task statements, O*NET 31.0 Database

  1. Assign the patient to diagnosis-related groups (DRGs), using appropriate computer software.
  2. Compile medical care and census data for statistical reports on diseases treated, surgery performed, or use of hospital beds.
  3. Design databases to support healthcare applications, ensuring security, performance and reliability.
  4. Develop in-service educational materials.
  5. Evaluate and recommend upgrades or improvements to existing computerized healthcare systems.

AI exposure

How much of the work large language models touch

53%

High exposure (β, human-rated)

Share of the occupation's tasks an LLM could speed up by at least half at equal quality, with software-dependent tasks at half weight.

Eloundou et al., Science 2024

43.6%

Augmentation share of AI use

Share of global Claude.ai conversations on these tasks with a classified collaboration pattern (excluding unclassified conversations) where AI worked with the person rather than doing the task.

Anthropic Economic Index (May 2026)

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. Emerging roles are the 30 occupations in the BLS fastest-growing occupations table (Table 1.3, 2025–35), ranked by projected percent change.
  2. Growth, openings, wage, and entry education are the BLS figures as published; openings include growth plus replacement needs.
  3. Tasks are O*NET 31.0 core task statements ranked by importance; skills are O*NET importance ratings (1–5).
  4. 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'.
  5. 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.

  • 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.

  • 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.

  • 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 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.

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