April 1, 2025
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3
Min Read
Creating edit checks for a clinical trial can be tedious and prone to human error. Clinical data managers spend hours (days? weeks?) writing complex code to make sure no bad data slips through the cracks.
Think: does this participant meet age requirements; do these lab values make logical sense; does the treatment date precede enrollment date.
It can be cumbersome, time-consuming, and, let's face it, sometimes kind of boring! So, instead we’ve added AI to do some of the hard work for you.
Say Hello to Your New Best Friend: Curebase’s AI Edit Check Generator ✨
Our AI Edit Check Generator takes away 70% of the manual work of developing edit checks for your clinical trial by turning simple instructions into usable code… instantly!
How It Works (Spoiler: It's Incredibly Simple)
- Type what you need, based on the study protocol in regular, human words
- Watch our AI generate the code in seconds
- Review the code and ensure it’s correct and make any adjustments necessary
- Click to approve (that's it, seriously) ✅
- Your edit check is live and ready to catch data issues
What Types of Edit Checks Can the AI Code?
I’m so glad you asked.
Demographics Edit Checks:
- Age restrictions (see our demo below!)
- Gender-specific requirements
- Location-based checks
Clinical Data Checks:
- Blood pressure checks (see our demo below!)
- Temperature readings that are actually humanly possible
- Complex questionnaire scores
Protocol Adherence Checks:
- Visit window validations (no, you can't do Visit 3 before Visit 2!)
- Dose calculations
General Formatting Checks:
- Required formatting for numbers or text
- Date validation
Add It to the Library for Next Time 📚
The best part? You can add the edit checks programmed by the AI to your library for use again. This feature is a gamechanger for clinical data managers running multiple studies!
Think about it: how many times have you created almost identical edit checks across different studies? With our library function, your days of redundant programming are officially over.
Here's what makes this so awesome:
♻️ Create once, reuse forever: did you build the perfect age restriction check? Save it to your library and drag-and-drop it into every study that needs it
💡 Your team's collective genius, archived: Everyone benefits from a growing collection of ready-to-use edit checks that actually work
✏️ Tweak without starting over: Need to adjust that BMI calculation slightly? Pull it from your library, make your quick edit, and you're done in seconds
See How it Works in 2 Short Demos
Watch how our AI Assistant generates an edit check faster than you can say "data validation” - with the 2 examples below.
"Write an edit check to see if diastolic blood pressure is outside of a reasonable range."
"The participant cannot be older than 75 years old."
This is how we're enhancing clinical trial setup at Curebase. Our AI assistant handles the heavy-lifting when it comes to the technical work so your team can focus on what matters - running successful studies and getting treatments to patients faster.
Ready to Learn more?
Discover how Curebase's EDC platform with our AI-powered edit check generator can streamline your workflows. Our tech is helping sponsors, CROs, and sites run smoother studies while keeping data squeaky clean!
Explore the Curebase EDC system.
Ready for a 1:1 Demo?
Schedule a demo with our team.
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AI-Powered Edit Checks for Clinical Trial Data Validation FAQ
What are edit checks in electronic clinical data management (EDC) systems?
Edit checks are validation rules integrated into EDC systems that ensure data quality by identifying and flagging potentially erroneous or inconsistent values before they impact study outcomes.
When are edit checks typically created during a clinical trial?
Most edit checks are implemented during the study setup phase, prior to participant enrollment. However, additional checks can be added throughout the study as needed to address emerging data patterns or requirements.
Who normally creates data edit checks for a clinical trial?
Traditionally, edit checks require programming by data managers, clinical programmers, or data management specialists with technical expertise in the specific EDC system being used.
What types of edit checks are commonly used in clinical trials?
Common types include range checks (for acceptable value boundaries), logical checks (for internal consistency), cross-form checks (for relationships between different data points), date checks (for chronological consistency), and missing data checks (for required fields).
How do edit checks improve data quality for clinical trial data?
Edit checks provide immediate or scheduled validation of entered data, allowing issues to be identified and corrected promptly, which prevents cascading data quality problems and reduces the need for extensive data cleaning later in the study.
What's the difference between hard and soft edit checks?
Hard edit checks prevent data entry completely when specified criteria aren't met, while soft edit checks flag potential issues but allow data entry to proceed with appropriate documentation or explanation of the discrepancy.
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