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AI agent for clinical data managers

Clinical Edit Check Validation Agent

Edit checks proven to catch the right data before the study goes live

Quality controlIT and development
See how it worksGet access
Clinical Edit Check Validation Agent: what goes in, what the agent does and what you get

What it does

Edit checks are the rules that catch bad clinical data, and if they are wrong, bad data passes while good data gets queried. Before a study goes live, this agent reads the data management plan and the programmed checks. It creates test records that should pass and records that should fail each rule, including boundary values. It runs them through the database and checks that each rule fires exactly when it should and stays quiet when it should. Where a rule fires wrongly or misses a case, it records the rule, the test record and the expected result for the programmer. After fixes it reruns the full set, not just the fixed rule. It builds the validation evidence for the study file. You approve moving checks to production. Edge case: two rules that conflict on one field are flagged together.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedNo 1 STARTS WHEN Edit checks ready for testing 2 DOES Create pass and fail test records for each rule 3 USES A TOOL Run the records through the test database 4 CHECKS THE RESULT Did each check fire exactly when it should? If not: record the rule, test record and expected resultfor the programmer, then rerun. Back to step 2. 5 DOES Flag any conflicting rules on the same field 6 DOES Build the validation evidence package 7 YOU APPROVE Data manager approves moving checks to production 8 RESULT Validated edit checks with evidence
Read the steps as a list
  1. Edit checks ready for testing
  2. Create pass and fail test records for each rule
  3. Run the records through the test database
  4. Did each check fire exactly when it should?If not: record the rule, test record and expected result for the programmer, then rerun. Back to step 2.
  5. Flag any conflicting rules on the same field
  6. Build the validation evidence package
  7. Data manager approves moving checks to productionThe agent waits here for your OK.
  8. Validated edit checks with evidence

How it decides

Each check must fire on every should-fail record and stay silent on every should-pass record before it is accepted.

  • Accept a check only when pass and fail cases both behave
  • Flag conflicting rules together
  • Keep test records and results as evidence

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Test cases per rule type
  • Boundary values to always test
  • Evidence format
  • Rules in scope

What keeps you in control

It always asks you first

  • Promoting edit checks to production

Hard limits

  • Tests only in the test database
  • Does not promote checks without approval

It stops when

  • Done: all checks validated with evidence
  • Stop: expected behavior is not defined in the plan

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensDuring validation for study CV-108 in March, a rule meant to flag ages over 65 fired at exactly 65. The boundary test caught it, and the agent recorded the expected result for the programmer. After the fix it reran all 80 checks, and all passed. Two weight rules still conflicted, so it flagged them together. The lead data manager approved release once they were reconciled.

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