Adapting to Modern Clinical Data Management Methods Requires Process Changes | The Future of Clinical Data Management Series
This blog was authored by Nicole Pollard, Principal Engagement Consultant, Strategic Consulting, Medidata.
As described in a previous post, the COVID-19 pandemic has expedited the interest in and adoption of decentralized trial technology. With this monumental shift, new and updated technologies are likely the best solutions to address aspects of the significant changes made to ICH E6 (R3), decentralized trials, patient diversity, data abundance, and clinical complexity. Implementing these technological solutions will require process and cultural changes.
“The life science industry has seen accelerating interest and adoption of decentralized trial technology in the wake of the COVID-19 pandemic…Sponsors and CROs are increasingly turning to decentralized trial models in an effort to bring increased efficiency, security, and accessibility to the clinical research process.”
– Anthony Costello, CEO, Patient Cloud at Medidata
For example, any gaps due to data re-entry, multiple systems, and resulting data latency or data errors create unacceptable risk. The velocity of decentralized clinical trial data capture requires monitoring tools that put sponsors and CROs as temporally close to the data entry as possible.
Sponsors and CROs have extensive access to process experts who can analyze existing states, design future state processes that maximize value from the technologies chosen, and create robust implementation plans to ensure organizational alignment.
Overall, the future is no longer years away—it has arrived! Both the ICH E6 R3 release and COVID-19 have provided the fuel for data management organizations worldwide to realize that to be sustainable, data collection and data cleaning will have to be swift, flexible, risk-based, and quality-driven.
ICH E6 (R3) provides guidance regarding the identification of critical data during the protocol writing process, the risks associated with these critical data, and the likelihood of such identified risks occurring. The FDA has endorsed these ideas and expects that clinical operations, as well as data management, will work in this critical data risk-based quality management approach. COVID-19 taught us that the typical office visit and site data entry may not look the same in the future. Data could come from a variety of technologies, and clinical data managers will have to integrate and reconcile the critical pieces.
Case Study: Process Change Related to the Timing of Clinical Trial Risk Management
A sponsor had historically delayed structured risk identification and mitigation discussions until the conduct phase of the study after subjects had already been enrolled. The sponsor found that when they shifted the timing of their clinical trial risk assessments to take place before the final protocol approval, they generated both a higher quality and cost savings. In short, the risks identified during protocol design allowed study teams to “de-risk” the draft protocol, thus decreasing the number of avoidable protocol amendments. For instance, one study team included an additional third-party lab to track an important KRI that was brought up during the risk assessment.
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