Decentralized clinical trials (DCTs) make it possible to conduct investigations away from the confines of traditional clinical research centers. They help broaden the scope of Veristat clinical research, and clinical trial design to previously underserved communities while enabling the adoption of tools like telemedicine and digital health for frequent remote patient monitoring in actual settings. This drive behind DCTs has created worries among sponsors regarding how to gather information remotely while maintaining data dependability and quality. The adoption of these technologies has made the planning and procedures necessary to guarantee data quality more difficult. Additional training and technological considerations could be needed to implement technologies like eConsent.
Redefine data quality
The data quality has always been linked to on-site monitoring and complete source data verification (SDV) requirements. However, research has revealed that SDV isn’t always equivalent to high-quality data. Targeted monitoring and decreased SDV have been on the rise even before the epidemic, which reduces the number of data points research associates needs to verify against local source data. The spread of technologies enabling remote data collection, monitoring, and study-related evaluations could hasten the adoption of new criteria for measuring data quality during the clinical trial planning stage.
Recognizing issues with data quality within DCTs
Clinical research always has problems with data quality and accuracy. The possibility of transcribing mistakes exists when using conventional paper-based methods for manually entering data from an electronic medical record into an electronic data capture system. Patient-reported outcomes (PROs) collected on paper raise questions about recall bias and missing data. Apps, ePROs, and wearable tech help make patients more comfortable, offer real-time data, and lighten the load on medical facilities. However, it can also call for alternative data collection methods, management, and compliance with changing legal requirements. Additional training and technological considerations could be needed to implement technologies like eConsent.
Maintaining data quality in a changing environment
Risk-based quality management (RBQM) is now more important than ever in today’s shifting clinical trial scenario. RBQM is a method for improving the quality and results of clinical trials by prospectively identifying and managing risk throughout a study’s full lifecycle and across all roles. The RBQM implementation process is centered on reviewing the trial’s goals, identifying the elements essential to attaining those goals, and developing a strategy to avoid having risks to those elements that hurt the results. Systems with RBQM capabilities are created to defend against both known and unidentified threats proactively. The RBQM technology offers reliable control for timely risk detection and management when used properly as part of your clinical trial. Continuous risk monitoring can help identify problems that might not have otherwise been seen, as well as early problem clarification and mitigation.
Facilitating the transition to DCTs
Patients, sites, and sponsors can all gain from the use of DCT techniques. Successful implementation of these approaches necessitates the careful evaluation of the processes, regulatory guidance, and technologies required to manage risk and ensure data quality. This cross-functional, multi-stakeholder endeavor could result in further downstream advantages such as improved patient participation, improved data quality, improved safety, less site burden, and a higher probability of trial success.
The use of technology in clinical trials has many benefits, including improving the effectiveness and quality of clinical investigations and promoting the development of customized medicine by boosting the number of possible predictive biomarkers with granular participant-monitoring techniques.