Improve Data Quality With 5 Fundamentals of Clinical Data Management

Gathering high-quality, reliable, and statistically sound data is the goal for every clinical trial, and effective data management is essential to ensuring accurate data collection, entry, reports, and validation. As a critical phase of the clinical research process, it’s important to establish and maintain organization-wide standards for data management to ensure consistency across all individuals and teams involved.

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. These five fundamental elements of quality clinical data management can both improve your organization’s data management standards and help you implement them across necessary stages of the clinical trial process.

1. Ensure data is “fit for purpose”

Professional societies, like the Society for Clinical Data Management (SCDM), advise organizations to establish standard practices that produce “fit for purpose” data sets, i.e., quality data. Fit for purpose methodologies imply that data quality improves when the data collected becomes more targeted to the study objectives. Eliminating non-critical data points lowers risk during endpoint analysis and minimizes the effort required to verify non-critical data.1

To ensure fit for purpose data, organizations must clearly define critical data points and standardize their collection and monitoring processes. Guidance in these areas can improve clinical data integrity and reduce the variability of data quality among individuals and teams involved.

2. Identify critical data points

Critical data points are identified at the very beginning of the clinical trial process. To do this, you must determine what data you need to measure to answer the scientific question your study originates from. What is the quantitative definition of your end goal?

This fundamental part of the clinical trial process may seem straightforward; however, issues often arise when non-critical data is collected for additional purposes, such as patient safety and/or exploratory analysis. Though important in many respects, ensuring these data meet quality standards requires considerable effort for the data management team. In addition to identifying more targeted data points, standard operating procedures (SOPs) can help reduce some of the time and effort for data managers when working with large amounts of data, and ultimately improve overall data quality.

3. Establish detailed standard operating procedures (SOPs)

Fewer errors in data collection and reporting means less time spent investigating the cause and correcting the problem. Developing thorough SOPs can help increase the accuracy of data collection by clearly outlining organizational practices and role-specific responsibilities. This specificity helps get all involved staff on the same page, reduces the risk for error in data collection, and can make it easier to pinpoint the cause if and when errors occur.

The best SOPs are often developed collaboratively with multiple staff members at an organization. Involving all relevant staff ensures a better understanding of the tasks involved in data collection and the current practices in place at your organization. It’s also an opportunity to document the best elements of current practices while identifying and course-correcting areas that may deviate from best practices.

4. Invest in staff education

One hurdle to developing SOPs can be a lack of visibility into best practices across the industry. Because research organizations must create their own standard procedures, it’s not always clear how your organization’s data management practices compare to peer organizations. There could be best practices you haven’t considered.

Investing in staff education can help keep your organization up to date on data management practices throughout the industry. Non-profit societies, like SCDM, provide opportunities to connect with other data management professionals, as well as tools and resources for implementing quality organizational standards.

5. Find the right system for your operational needs

While developing standard procedures that align with industry best practices is critical to improving clinical data collection and quality at your research organization, efficiency in the clinical trial process is often only as good as the systems you choose to implement. When it comes to data management, electronic data capture (EDC) systems should encourage organizational best practices for data quality rather than deter them. The best EDC systems are easy-to-use and intuitive for all staff members, and ultimately reduce the potential for error when reporting into the system.

Your EDC system should be secure, minimize improper data collection, and allow you to effectively export your data. Some systems, like Advarra EDC, offer features such as edit checks, visit and timepoint tolerances, and conditional forms increase the integrity of your clinical data.

If you’re conducting investigational new drug (IND) studies, it’s also important that your EDC system is validated and compliant with 21 CFR Part 11. Among other benefits, like meeting the requirement for electronic signatures, a validated system allows for stronger communication between vendor systems and increases the value of your study data for sponsors/CROs.

Need an easy-to-use EDC system?

Advarra EDC allows users to build custom forms, set up edit checks and use forms across multiple protocols to decrease duplicate data entry and error. Learn more about how Advarra EDC can streamline your data collection, management, and compliance. Start the conversation.

Resource

1. http://www.appliedclinicaltrialsonline.com/how-do-we-define-clinical-trial-data-quality-if-no-guidelines-exist

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