Analyze health-related data.

Analyze health-related data. is a detailed work activity in O*NET — a concrete unit of work shared across 9 occupations and seen in 14 occupation-specific tasks. It rolls up into the broader work activity Analyze health or medical data. in Analyzing Data or Information .

Detailed work activities are the most granular shared layer in O*NET's work-activity hierarchy (Generalized → Intermediate → Detailed → occupation-specific task). The figures below describe how this activity shows up across the economy and what independent studies measure about AI and this kind of work — not a prediction that the work will be automated.

AI exposure

Of the 14 tasks under this activity that the OpenAI / Eloundou “GPTs are GPTs” study rated, 14 (100%) are flagged as directly exposed to language models (E1) or exposed via model-powered tools (E2).

The Anthropic Economic Index observes real AI use on 7 of these tasks, with a mean mapped-usage share of 0.008% per task.

Exposure estimates overlap with model capabilities — whether a model could speed the task up — not whether the work will be done by software. Observed AI use is augmentation and assistance today, not jobs replaced.

Member tasks

Occupation-specific tasks O*NET maps to this detailed work activity, most important first.

• Calculate the delivery of radiation treatment, such as the amount or extent of radiation per session, based on the prescribed course of radiation therapy. · Medical Dosimetrists · importance 4.8 · exposure with tools
• Calculate, or verify calculations of, prescribed radiation doses. · Medical Dosimetrists · importance 4.8 · direct LLM exposure
• Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property. · Insurance Underwriters · importance 4.5 · exposure with tools
• Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates. · Actuaries · importance 4.4 · exposure with tools
• Analyze prescribing trends to monitor patient compliance and to prevent excessive usage or harmful interactions. · Pharmacists · importance 4.4 · exposure with tools
• Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques. · Biostatisticians · importance 4.4 · exposure with tools
• Analyze and interpret patient, nursing, or information systems data to improve nursing services. · Health Informatics Specialists · importance 4.3 · exposure with tools
• Develop treatment plans, and calculate doses for brachytherapy procedures. · Medical Dosimetrists · importance 4.3 · exposure with tools
• Identify, collect, record, or analyze data relevant to the nursing care of patients. · Health Informatics Specialists · importance 4.2 · exposure with tools
• Conduct research to develop methodologies, instrumentation, and procedures for medical application, analyzing data and presenting findings to the scientific audience and general public. · Medical Scientists, Except Epidemiologists · importance 4.1 · exposure with tools
• Identify and analyze public health issues related to foodborne parasitic diseases and their impact on public policies, scientific studies, or surveys. · Epidemiologists · importance 3.9 · exposure with tools
• Analyze clinical data using appropriate statistical tools. · Clinical Data Managers · importance 3.7 · exposure with tools
• Conduct research to develop methodologies, instrumentation, and procedures for medical application, analyzing data and presenting findings. · Epidemiologists · importance 3.7 · exposure with tools
• Analyze archival data, such as birth, death, and disease records. · Biostatisticians · importance 3.4 · exposure with tools

Occupations that perform this

• Medical Dosimetrists
• Insurance Underwriters
• Actuaries
• Pharmacists
• Biostatisticians
• Health Informatics Specialists
• Medical Scientists, Except Epidemiologists
• Epidemiologists
• Clinical Data Managers

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-analyze-health-related-data,
  title  = {Analyze health-related data.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/detailed-activities/analyze-health-related-data}
}