Gathering high-quality, reliable and statistically sound data is the goal for every clinical trial; the way data is collected and managed has an impact in every stage of the clinical trial process. Miscommunication and ambiguity in the data management process can lead to costly mistakes that waste staff time and resources, ultimately putting your organization and clinical trial at risk.
Effective data management is essential to ensuring accurate data collection, entry, reports and validation, and it’s critical to establish and maintain organization-wide standards for data management, ensuring consistency across all individuals and teams throughout the trial lifecycle.
Data contributed in each stage of the clinical trial process impacts the clinical trial as a whole. By following good clinical data management practices at each stage, you’re improving the reliability of the data, the study itself, and ultimately the potential benefit to the community of patients.
This infographic shows how quality data practices can influence critical stages of the clinical trial process.
Pre-Trial Activities: Activation & Enrollment
- Establish SOPs: Set the clinical trial up for success by documenting standard operating procedures (SOPs) for clinical trial activities to ensure consistency in data management.
- Staff training: Emphasize data integrity at the very beginning of a clinical trial to create good data collection habits among trial staff.
- Critical data points: Identify what data you need to measure the scientific question from which your study originates. Ensure the data you gather is ‘fit for a purpose,’ preventing unnecessary data collection and opportunity for error.
- Define performance metrics: You can’t improve what you don’t measure. Decide what data quality metrics are important to you to measure performance.
During the Trial: Data Collection
- Run reports: Regular reporting and communication with staff helps identify issues early on in the data collection process.
- Consistent communication: Discussions about data integrity shouldn’t end with initial training. Continuously communicate the importance of data quality with staff.
- Reduce errors: Humans make mistakes. Any steps taken to reduce errors such as timely data entry and edit checks on forms helps make data more accurate.
- Data review: Implementing peer review or spot checking of your data prior to auditing helps put one more check in place.
Post-Trial Activities: Closeout & Analysis
- Team engagement: Have discussions with leadership and all teams on what went well and what could be improved regarding data integrity.
- Assess performance metrics: Revisit the metrics you defined pre-trial and identify areas in need of attention.
- Staff feedback: Clinical trial staff are on the front lines of data collection. See how their experience was and what their suggestions are for improvement.
- Process improvements: Once you know what you want to do better, create action plans to make future trials successful.
Using an electronic data capture (EDC) software is one way to ensure higher data quality. Learn more by requesting a demo of Forte EDC for your organization.