The Past, Present and Future of Clinical Trial Data Management

At the recent Engage annual conference, leaders from eClinical Solutions took the opportunity to host a roundtable discussion around the evolution of clinical trial data management. The panel included:

• Bethann Schrader-Giancarlo, Director of Data Management at Pharvaris, a specialized pharmaceutical company focused on developing novel, oral alternative medications that improve the standard of care for people living with hereditary angioedema (HAE)
• Emily Pereira, Director of Clinical Data Management at bluebird bio, a sponsor pursuing curative gene therapies for serious conditions like sickle cell disease, β-thalassemia and cerebral adrenoleukodystrophy
• Sherry Volk, Principal Portfolio Lead at eClinical Solutions
• Moderator – Diane Lacroix, Vice President, Clinical Data Management at eClinical Solutions

The discussion delved into the changing landscape of clinical data management, the ongoing impact of technological advances, and the future of data management in the industry.

Transformation in Clinical Data Management – Key Challenges and Solutions

Diane Lacroix: It is wonderful to have this talented group with us to talk about the changes we are seeing to clinical trial data management. I think it’s important to reflect on the fact that it wasn’t that long ago that we were using paper and filing it in fireproof cabinets. The transformation to EDC was a significant shift and now we’re seeing a similar moment of change where data is now coming directly from a huge range of sources, not just from an EDC. To kick things off, I would like to ask you all, what do you see as the key changes we need to make to support modern clinical trials, and what do you feel are the barriers to making those changes?

Bethann Schrader-Giancarlo: With the increase in data streams that we’re seeing, I think we need real-time access to data and analytics. We need to be able to ingest and understand data quickly, at a glance so that we can make more informed decisions. In terms of barriers, I think that the perceived cost for tools is still a problem.

Emily Pereira: For me, I think it is the sheer amount of external data that is coming in now. The amount of data sources and the different kinds of data make ingesting it and having it easily accessible a challenge. Particularly for what we do in gene therapy, where data can come in non-standard formats like PDFs. Efficient data management tools are essential for integrating and utilizing these diverse data streams across all our studies.

Sherry Volk: To echo the rest of the group, it really is important to have tools that effectively bring together various data sources and free up other resources for higher-level data analysis. As already noted, real-time data access and analytics are critical for enabling quicker decisions. We are working at eClinical Solutions to make sure that elluminate^® is always ready to help provide researchers with easier, faster access to high quality data. And we are transforming our Biometrics Services offering, moving toward a clinical data science model, allowing closer collaboration with research teams and powering future innovations.

Risk-Based Approaches and Data Quality

Lacroix: Risk-based monitoring has taken hold and is now widely adopted throughout the research industry. However, these accepted methodologies appear to be lagging behind when it comes to data management. Despite this continued messaging from regulatory bodies that we need to focus on critical data and critical processes, we still seem to be edit checking and cleaning every data point. How do we begin to shift this mindset and begin taking advantage of these risk-based approaches?

Schrader-Giancarlo: I was working in the oncology space and looking at vital signs when I noticed that we had thousands of queries firing. We believed we had set a normal range for very sick patients, but out of the thousands of queries, there were only 100 or so that resulted in a change. So, we worked with our medical monitors to better specify an accurate range of comfort for a vital sign that’s not an endpoint. This reduced the impact on data managers, the cost of edit checks, and the burden on sites who had to constantly follow up on these unnecessary queries. It really comes down to focusing on primary endpoints and adopting a more fit-for-purpose mindset.

Pereira: At bluebird we have a really robust cross-functional data review process and we use elluminate. We have hundreds of listings programmed that really focus on those key safety and efficacy endpoints. We’ve had success leveraging our cross functional team from our medical reviewers to our statisticians, clinical scientists, data scientists, and others to zoom in on exactly what we want to review. It helps to be able to use a tool like elluminate that helps to visualize listings and draw out the outliers for easier monitoring. It’s just an amazing timesaver.

The Evolution of Clinical Data Management and Clinical Data Science

Lacroix: The roles in clinical data management are evolving, with a trend towards clinical data science. What do you see as the key differences in the skill sets of a data manager or clinical data scientist today versus how the role has looked in the past?

Pereira: I think that today’s data managers are now more involved in clinical aspects, aiding cross-functional groups and gaining a deeper understanding of trials and patient data.

Schrader-Giancarlo: I was struck by an analogy. I feel like data managers are the cowboys who chase the cattle and make sure they get where they’re supposed to go. Then the data scientists are the person in the helicopter with the bird’s eye view of what’s happening and a more complete understanding of the purpose of the cattle drive itself.

The Future of Clinical Data Management

Looking ahead, each of the panelists agreed that artificial intelligence and machine learning (AI/ML) will significantly impact data management in the near and long-term future.

Schrader-Giancarlo: Decentralized clinical trials (DCTs) will continue to grow, but slowly. I believe AI/ML is already having a tremendous impact on what we do and that will just increase as time goes on.

Pereira: I agree. These technologies will become more common and necessary as data points continue to increase at these levels.

Fortunately, the team at eClinical Solutions is already positioned to help researchers seeking to integrate AI/ML to help solve these challenges that are shaping the future of clinical trial data management. The eIQ Review (eIQ) offering provides AI-enabled data review capabilities to ensure that data managers and reviewers can effectively scale up for larger data volumes while maintaining data integrity. So as the future promises even more data from even more sources – some that have not even been conceived of yet – eIQ can introduce new efficiencies so that researchers can be confident that anomalies will be detected quickly and that troubling data trends will be identified in real time. Better yet, eIQ makes getting started with AI simple, as it is focused, measurable, and easy to use.

Moving Data Forward

The insights shared at Engage 2023 by these expert data management professionals underscore the dynamic nature of clinical data management. It is truly an exciting time to be working in this space. As the field continues to evolve, embracing new technologies and adopting risk-based approaches will be crucial for managing the increasing complexity and volume of clinical trial data. The future of clinical data management lies in the integration of advanced tools, strategic thinking, and continuous adaptation to the changing landscape of clinical research. The team at eClinical Solutions is prepared to help researchers move into this future with confidence, through the flexibility of the elluminate Clinical Data Cloud and our Biometrics Services.

Author

Stephanie Emanuele

Sr. Marketing Manager, eClinical Solutions