Home » Clinical Trials » Rethinking data quality best practice in the era of decentralised clinical trials

Stacy Well

Senior Vice President, Clinical Data Operations, Premier Research

Pandemic-related disruptions have accelerated much-needed change in clinical operations, but this change has been accompanied by questions about data collection and data quality. 

In a recent survey commissioned by Oracle Health Sciences, more than 75% of industry respondents indicated that limitations in patients’ ability to attend on-site visits sped up their adoption of decentralised clinical trial (DCT) approaches. This momentum behind DCTs has, however, raised concerns among sponsors about how to collect data remotely while ensuring data reliability and quality

Understanding challenges to data quality in DCTs 

Challenges related to data accuracy and quality are intrinsic to clinical research. With traditional paper-based methods, manual entry of information from the electronic health record into the electronic data capture system creates the risk of transcription errors. The collection of paper-based patient-reported outcomes (PROs) comes with concerns about recall bias or missing data

The use of apps, electronic patient-reported outcomes (ePROs) and wearable devices may increase patient convenience, provide real-time data, and reduce site burden. Still, it may also require different approaches for collecting and managing data and complying with evolving regulatory guidance. Implementing technologies such as eConsent may involve additional training and technical considerations. 

Challenges related to data accuracy and quality are intrinsic to clinical research.

Ensuring data quality in an evolving landscape 

In this evolving clinical trial landscape, risk-based quality management (RBQM) is more relevant than ever. RBQM is an approach to prospectively identify and manage risk across the entire lifecycle of a study—and across all roles—to improve clinical trial quality and outcomes. The process of implementing RBQM focuses on examining the objectives of the trial, defining critical factors to achieving those objectives, then creating a plan to prevent risks to those factors from negatively impacting outcomes. 

RBQM technologies are systems designed to proactively protect against potential threats, both known and unknown. When appropriately implemented as part of a clinical trial, RBQM technology provides robust control for early risk detection and prevention. Continual risk monitoring can lead to earlier clarification and mitigation of problems and even identify issues that might not otherwise have been detected.  

Enabling the shift to DCTs 

Adoption of DCT approaches can benefit patients, sites and sponsors alike. Successful implementation of these approaches requires careful consideration of the regulatory guidance, processes, and technologies necessary to ensure data quality and manage risk. This is a cross-functional, multi-stakeholder effort that may lead to the added downstream benefits of increased patient engagement, enhanced data quality, improved safety, reduced site burden and a higher likelihood of trial success. 

The original article was created by Stacy Weil, Debra Jendrasek, LaRae Bennett and Joan Sutphen-Glowatz | March 3, 2021 https://premier-research.com/blog-rethinking-data-quality-best-practices-in-the-era-of-decentralized-clinical-trials/  

Next article