Too Fast, Too Furious: How Can Data Managers Keep Up with the Shifting Landscape of Clinical Data Management?
Medidata’s first webinar series for 2022 was designed to bring together industry leaders to discuss the trends and insights in modernizing clinical data management. We would like to thank all attendees that participated in the first webinar of the series and featured Nicole Pollard (Managing, Principal Engagement Consultant, Medidata) and Katrina Rice (Chief Delivery Officer, Data Services, eClinical Solutions).
Organizations are being forced to rethink what clinical data cleaning means and enable new ways of implementing the best approach to collect, clean, and report on the data in their clinical trials. The webinar explored strategies that clinical data managers can use to elevate their data quality management in a rapidly-evolving environment, including:
- Use of visualizations and analytics
- How to make the most of current tools and how to update processes to meet today’s needs
- How to execute change, optimize and streamline processes, and use analytics for a true, risk-based clinical data management approach
Thank you again to all who took part in the webinar. As requested by some of our attendees, below is a recap of the webinar Q&A which provides additional insights on how organizations can adapt to the mounting clinical data pressures of modern clinical trials.
Will these analytical systems, like eClinical Solutions, replace electronic data capture (EDC) as we head toward decentralized clinical trials (DCTs)?
Katrina Rice: The eClinical Solutions platform, “elluminate® Clinical Data Cloud,” is not an EDC nor is it intended to replace EDC. With the shift to decentralized clinical trial technologies to acquire clinical data, we expect some of the historical functionality of EDC to decrease as we move toward more patient-centric data collection methods. But I foresee this as still a bit in the future, and it will be driven heavily by the therapeutic area. Some therapeutic areas will still need EDC or have processes that can’t be done outside of EDC.
How do you best recommend streamlining or combining siloed data streams?
Katrina Rice: The move to decentralized clinical trials to acquire clinical data and the increase in data streams from external data providers and systems means that it will be critical to have clinical platforms that can manage these various streams. I recommend that companies invest in a clinical data aggregation and analytics platform, such as the elluminate® Clinical Data Cloud, to ingest, standardize, and make available for analysis these incoming streams.
How are regulatory agencies vetting and validating these new clinical data management technologies (data visualizations, trends, AI, and machine learning)? It will take time to support the implementation of these data management technologies and to build processes around these. How are regulatory agencies viewing these data management systems?
Katrina Rice: We see, based on what is happening in the industry, that regulatory agencies are supportive and in favor of new technologies. We have good guidance at this point and it is catching up to the technologies faster than ever. Spurred on by the COVID-19 pandemic, the FDA and other regulatory agencies have increasingly shown that they support clinical trial technologies that increase efficiency, improve quality, and reduce risk.
Much of the guidance in recent years, including the FDA’s draft guidance from January 2022 about digital health technology, provides evidence of this support. Work is involved to operationalize and build or adapt processes with this shift, but the idea that regulatory agencies deter sponsors and CROs from adopting digital technologies is often overstated.
With the clinical data manager’s role becoming complex and multi-pronged—being responsible for data collection, stewardship, cleaning, governance, and technological solutions—how do you envision pharma/biotech and CROs supporting hiring and having focused teams for technology-enabled data management?
Katrina Rice: The industry faces the challenge of having enough resources and time to handle the ongoing work, when we all know that recruiting, training, and upskilling takes time. The need is high, the pace is fast (and growing faster every day for today’s trials), and skilled resources are insufficient. The next phase is growing and elevating the next generation of clinical data management leaders; this takes time.
Technology companies can also enable these processes and make them easier. It’s important that tools allow for easy configuration and start-up. Technology companies can enable efficiency and smooth data management skills and tech adoption process by making adoption easier through training, on-demand learning, and ease of use.
Cultural change is the second type of change that must be managed. Teams need to adjust their thinking and priorities regarding their role in the clinical trial. Although it is tempting to merely state that all clinical data must be reviewed and validated, it is better to communicate the positive results of adhering to the new guidance and recommendations that risk-based 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 of disparate data sources
- Decreased mundane manual activity through access to technology
- Faster cycle times from last patient, last visit to a submission-ready database
Although it may take time before you truly see the full results of the effort, making these changes requires reviewing current processes and workflows and leaning into the early efficiency gains and wins that technology-enabled approaches can offer. That will realize the full cultural change of technology adoption and team hiring and design. Full management support is critical.
How would you recommend a clinical data manager use their time to both 1) manage these increasingly complex data flows and integrations that may be continuously disrupted by complex and adaptive protocols and 2) take on all the additional competencies and activities that come along with understanding and performing higher-level data analytics and review?
Katrina Rice: In my view, this is not necessarily a personal time management issue; the company needs to support that with a cultural shift.
Do you have any example of a technical skill that may be requested for clinical data managers to better cover/understand/manage the technologies for DCTs?
Katrina Rice: I don’t see that the technical skills required to manage DCTs are any different from the skills necessary for EDC or any other external clinical data source. Most of it is coming directly from the patient (that is the primary difference), but as far as the technical skills required, it’s still data. What will be different is the management, query, and cleaning of that data. It will be about trying to identify trends and make judgments to share with the various data stewards, which is more about new workflows rather than new technical skills specific to DCTs.
I'm particularly interested in how the current trends or practices around having very large studies—how risk-based data cleaning or flagging areas of concern—are evolving. Are there any thoughts or future plans by organizers toward making clinical data management training and technical capacity development affordable for aspiring clinical data professionals, especially in low- and middle-income nations?
Katrina Rice: Many resources are available from associations and online for personal development at low or no cost. In addition, companies will need to have a role in the training and upskilling related to the shifting responsibilities.