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Adapting to Modern Clinical Data Management Methods Requires Cultural Changes | The Future of Clinical Data Management Series

Jan 26, 2022 - 3 min read
Adapting to Modern Clinical Data Management Methods Requires Cultural Changes | The Future of Clinical Data Management Series

This blog was authored by Nadia Bracken, Senior Engagement Consultant, Solutions Services/RBQM, Medidata.


In addition to process changes that must be implemented to adapt to modern clinical data management methods, cultural changes must also be managed. Teams need to adjust their thinking and priorities to their roles in the clinical trial paradigm. Although it can be tempting to merely state that all data must be reviewed and validated, it is better to communicate the positive results that come from adhering to the new guidance and recommendations that risk-based quality management monitoring, data governance, and technological solutions bring, such as the following:

  • Early access to trial insights for mitigation
  • Improved identification of safety issues
  • Reduced review times for disparate data sources
  • Decreased mundane manual activity through access to technology
  • Faster cycle times from last patient’s last visit to a submission-ready database

Providing options that are adaptive with flexible “site” workflows and the ability to ingest data from various sources (i.e., telehealth, wearable devices) requires the analysis and restructuring of many clinical operations. 

The increase in data from multiple sources means that data management processes have to be modernized from onsite data cleaning and monitoring by using intelligent and automated tools for remote monitoring, analysis, and the cleaning of data. The major shift required post-ICH E6 (R3) has been to conduct oversight activities and produce artifacts of documentation to show that the oversight was done. 

After two decades of static guidelines for ICH GCP, new terminology has been introduced for risk identification and mitigation. Although it was clear that the risk-monitoring strategy would need to be adaptive, the ambiguity around how to operationalize this guidance led to industry working groups and toolkits for identifying Critical to Quality (CtQ) data and processes. 

Sponsors are informed in the revision to identify risks to critical trial processes and data and to evaluate the identified risks against existing risk controls considering the likelihood of errors to occur. This evaluation is coupled with an assessment of both the extent to which errors will be detectable and the impact of such errors on human subject protection and the reliability of trial results. From there, clinical trial sponsors and CROs struggle to define meaningful risk indicators and prospectively identify quality tolerance limits.

Essential to the process is that early access to data is needed to mitigate trial risks. This paves the way for mitigations and other interventions contemporaneously as issues arise. Quite possibly the biggest barrier to industry adoption of ICH E6 (R3) is a demonstrable reluctance to operate with reduced source data verification. The COVID-19 pandemic necessitated more centralized statistical monitoring and less reliance on traditional source data verification. Importantly, nearly every global regulatory body that released COVID-19-specific guidance noted the importance of performing risk assessment activities.

ICH E6 (R3) is significant because it will address the reality of many disparate data sources in modern clinical trials and expand guidance to include the use of technology to ensure the quality of the trials. Notably, the updated guidance includes an emphasis on the design of oversight, indicating that a one-size-fits-all methodology will be inadequate. The draft version includes Annex 1 (addressing interventional clinical trials) and Annex 2 (providing any needed additional considerations for non-traditional, interventional clinical trials). The overarching principles document and Annex 1 are intended to replace the current ICH E6(R2), and ICH E6 (R3) clarifies that clinical trial teams are to design quality into the study protocol and processes. These activities should be applied during the early planning stages and across trial operations. R3 is supportive of an improved and more efficient approach to trial design and conduct.

Case Study: Cultural Change Related to Targeted Source Document Verification (TSDV) Implementation

A sponsor implemented Medidata Rave TSDV and rolled out a targeted approach to verify only critical data points defined by the study team. The sponsor tracked value metrics to look at time and cost savings derived from clinical research associates. While they verified only a subset of source data using the targeted, risk-based quality management approach, they were surprised to see virtually no change in SDV levels across their studies.


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What Are Decentralized Clinical Trials and How Do They Impact Clinical Data Management?

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