Analyze clinical data using appropriate statistical tools.

“Analyze clinical data using appropriate statistical tools.” is a core task performed by Clinical Data Managers. Among the occupation's 21 rated tasks, workers place it 11th by importance (#11 most important). About 70% of workers say it is relevant to their job.

This is a single occupation-specific task statement from O*NET. The figures below describe how central the task is to the job and what independent studies measure about AI and this kind of work — not a prediction that the task will be automated.

Work activities this task rolls up to

O*NET groups concrete tasks into broader work activities shared across many occupations.

• Analyze health or medical data. in Analyzing Data or Information

AI exposure

The OpenAI / Eloundou “GPTs are GPTs” study rates this task E2. Exposure with tools — software built on top of a language model (not the model alone) could cut the time by at least half.

Exposure measures whether a model could meaningfully speed the task up — it is an estimate of overlap with model capabilities, not a measure of whether the work will be done by software. The study's intermediate score (β) for this task is 0.50. Automation potential label: T3.

How AI is actually used on this kind of task

The Anthropic Economic Index observes how people actually use AI on tasks like this one across millions of real conversations.

• 0.018% share of AI-use records mapped to this task
• 81% of interactions still needed a human in the loop

Observed AI use describes people choosing to use AI as a tool on this kind of task today. It is augmentation and assistance, not a measure of jobs replaced.

Other tasks in this occupation

• Design and validate clinical databases, including designing or testing logic checks. · importance 4.5
• Process clinical data, including receipt, entry, verification, or filing of information. · importance 4.4
• Generate data queries, based on validation checks or errors and omissions identified during data entry, to resolve identified problems. · importance 4.3
• Develop project-specific data management plans that address areas such as coding, reporting, or transfer of data, database locks, and work flow processes. · importance 4.3
• Monitor work productivity or quality to ensure compliance with standard operating procedures. · importance 4.0
• Prepare appropriate formatting to data sets as requested. · importance 4.0
• Prepare data analysis listings and activity, performance, or progress reports. · importance 4.0
• Design forms for receiving, processing, or tracking data. · importance 4.0
• Confer with end users to define or implement clinical system requirements such as data release formats, delivery schedules, and testing protocols. · importance 3.8
• Perform quality control audits to ensure accuracy, completeness, or proper usage of clinical systems and data. · importance 3.7
• Evaluate processes and technologies, and suggest revisions to increase productivity and efficiency. · importance 3.7
• Develop technical specifications for data management programming and communicate needs to information technology staff. · importance 3.6
• Write work instruction manuals, data capture guidelines, or standard operating procedures. · importance 3.6
• Track the flow of work forms, including in-house data flow or electronic forms transfer. · importance 3.5

See all tasks on the Clinical Data Managers page.

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-task-16283,
  title  = {Analyze clinical data using appropriate statistical tools.},
  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/tasks/task-16283}
}