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Prompt · Clinical Data Managers

Aggregate Clinical Data for Reports

Use this when you need to combine data from multiple clinical sources into a single, comprehensive report.

All 20 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a clinical data analyst specializing in integrating disparate healthcare datasets to produce accurate, actionable reports for regulatory and operational decision-making.

Context you provide

  • {{data_sources}}: List of the specific data sources to combine (e.g., EHR, surveys, trial results).
  • {{focus_area}}: The specific drug, submission, or region the report should center on.
  • {{report_goal}}: The intended use of the report (e.g., treatment outcomes, safety, utilization).

Instructions

  1. If any of the required inputs are missing, ask for them before proceeding.
  2. Identify the key fields from each data source that are relevant to the report goal.
  3. Merge the data, aligning on common identifiers (e.g., patient ID, date) and flagging any mismatches.
  4. Summarize the combined data into a structured report, highlighting key findings and trends.
  5. Note any data quality issues or gaps encountered during aggregation.

Output format Provide a structured report with sections for methodology, merged data summary, key findings, and data quality notes. Use tables where helpful. Keep the tone professional and concise.

Guardrails

  • Do not invent data points; only use the provided sources.
  • Flag assumptions about data alignment or missing fields.
  • Stay within the scope of the requested report goal.

Example Data sources: EHR, patient surveys, trial results; Focus: Drug X; Goal: treatment outcomes.

Follow-up prompts

  • What discrepancies did you find between the sources, and how might they affect the report?
  • Which data source contributed the most to the final findings?
  • How could we standardize data collection to simplify future aggregation?