Good data is essential for successful clinical trials. It helps ensure accurate analysis, guides important decisions, and supports the approval and safe use of new treatments. As trials become more complex with remote setups, many data sources, and stricter rules, keeping data quality high is more important than ever.

In this post, we’ll look at simple, effective ways to protect the accuracy and trustworthiness of data in clinical trials.

Create a strong data management plan

A good Data Management Plan (DMP) is the first step to quality data. It explains how data will be collected, checked, cleaned, and stored during the trial. It also helps everyone involved know their role.

A strong DMP should include:

• Clear roles and responsibilities
• Information about study set up including the electronic data capture used and the audit trail.
• Step-by-step instructions for entering and handling data
• Data cleaning process and details
• Management of Serious Adverse Event (SAE) reconciliation and medical coding within the study

If you start your DMP early and keep it up to date, it will help avoid confusion and keep the trial consistent.

Use standardized data collection methods

Data collection should follow a consistent approach. It starts with designing a smart Case Report Form (CRF) that only asks for the necessary information and matches the trial goals. Using standard forms (like CDASH) across studies makes data easier to manage and review.

Other ways to keep data collection consistent:

• Use standard medical terms (e.g., MedDRA, WHO Drug Dictionary)
• Train staff on correct data entry
• Use reliable electronic systems with built-in checks to catch errors
• Avoid the comment or text field as much as possible
• Do not collect data twice (duplicated data)

These strategies will reduce mistakes and save time fixing issues later.

Monitor data actively

To keep data quality high, you need to prevent problems and catch them early. Active monitoring either remotely or based on risk can help spot problems before they get worse.

Examples of active monitoring:

• Dashboards that show missing or unusual data
• Review of top priority data for the primary analysis
• Regular review of key items like side effects or medication use
• Focus monitoring on high-risk sites and processes

Finding and fixing issues early keeps your data reliable. Moreover, fixing problems as early as possible enables the site to avoid recurring issues.

Handle queries quickly and clearly

Resolving data queries (questions or issues) takes time, so it’s important to manage this well.

Tips for efficient query handling:

• Use automated checks to catch simple issues
• Focus manual review on complex or safety related data
• Keep clear records of how each query is resolved by adding a comment
• Pay attention to queries opened for several days to check with the sites on the reason why

Good query management keeps the trial moving and ensures the data is clean and complete.

Combine data from different sources carefully

Today’s trials often use data from many places like labs, apps, devices, and imaging systems. Keeping this data consistent is key.

Best practices include:

• Creating a Data Transfer Agreement (DTA) detailing data transfer specifications, like for instance how the data will be transmitted, the frequency of the transfer, and the data to be transmitted
• Checking and validating all incoming data
• Setting up checks to make sure sources agree (e.g., comparing lab data with system data)

Good data integration helps you understand results more clearly and trust the final data.

Follow regulatory guidelines

High data quality also means following the rules. Agencies like the FDA and EMA expect clean, traceable, and well-documented data.

To be compliant:

• Have clear procedures and test your systems
• Run regular data audits
• Make sure your data follows ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, and more)

Meeting these rules protects your study and shows the data is trustworthy.

Staff training and communication

Even with great tools, skilled people are essential. Train all team members regularly so they understand their role in protecting data quality. Make sure communication is clear between everyone’s sites, teams, and vendors. Write eCRF Completion Guidelines and perform a training video to train the sites and the investigator, explaining how the system works and how to perform data entry.

Sharing knowledge and working together helps build a culture where quality comes first.

Final thoughts

Keeping data quality high in clinical trials takes planning, careful checks, and teamwork. By following best practices like clear data collection, active monitoring, and smart integration, you can ensure your data is accurate and ready for review.

As clinical trials continue to evolve, one thing stays the same: quality data is key to faster approvals and better treatments for patients.

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