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05 Feb 2024
With new technologies emerging, evolving industry standards, and an increasing focus on both speed and quality, the world of clinical data management (CDM) is undergoing a profound transformation. Leaders in this space are rethinking how clinical trial data is handled from collection through regulatory submission.
The success of clinical trials hinges on three key factors: disciplined and precise clinical trial design, realistic participant enrolment timelines, and strict adherence to processes. When planning a new clinical trial, it is crucial to review past trial designs and clinical trial data management practices to understand critical success factors and implement new learnings. However, this process remains challenging and time-consuming due to its manual nature and the nascent adoption of digitalized libraries.
Moreover, traditional document preparation methods result in the generation of numerous documents, all of which must comply with international regulations and undergo rigorous scrutiny. Our latest survey reveals that 53% of clinical data management leaders cite "getting faster at SDTM mapping" as their number one priority, as faster SDTM mapping equates directly to accelerated insights, quicker regulatory submissions, and ultimately, more timely delivery of new treatments to patients.
Novel digital technologies, automation in clinical trials, and omnichannel outreach initiatives that leverage Real World Data (RWD) have significantly reduced the overall time and cost involved in designing and executing clinical trials. However, the potential impact of Generative AI in clinical trials is transformational. A recent report by BCG states that by using GenAI alongside human expertise, pharmaceutical companies can achieve up to 40% productivity improvement in such activities.
GenAI for Clinical Data Management: A Path to Improved Efficiency
In our recent webinar, “Generative AI and ML in Clinical Data Management: Decode the Future of Clinical Trials,” industry experts Dr. Santhosh Kumar and Mark Williams shared compelling use cases demonstrating how AI in clinical data management can significantly enhance the productivity of clinical trial data management activities.
Key benefits of using Generative AI in clinical trials include:
Build and Utilize Digital Libraries
Clinical trial data is highly structured and standardized, making it ideal for automation. By leveraging Natural Language Processing (NLP), users can create digital libraries that provide overviews of each protocol section based on selected criteria, and sort protocols by trial phase and design. This improves visibility and decision-making across the clinical trial data management lifecycle.Improve eCRF creation:
75% of CDM leaders see generative AI and ML as “key to greater efficiency” in clinical research, and more than 50% of companies are already utilizing AI and ML to enhance data quality and streamline studies, with the most common applications being automation of reports and accelerating database setups. With GenAI, users can automate the reading and understanding of protocols to generate electronic case report forms (eCRFs) efficiently. Leveraging existing digital libraries helps create eCRFs that align with protocol requirements, including features like versioning and audit trails to manage amendments. These eCRFs can be generated in table formats similar to how they appear on EDC clinical trials platforms, enhancing readability and analysis.Streamline data extraction:
Screening and extracting relevant protocol information is one of the crucial tasks a critical task in clinical data management. With GenAI, entire studies—including eCRFs—can be built to adapt to various electronic data capture (EDC) systems. Enhanced data extraction from historical protocols empowers better protocol design through intelligent search and analysis capabilities.Improve Data Management Activities:
Using GenAI, users can streamline documentation by automatically generating data management plans, edit check validation specifications, and test cases, all built using pre-existing standards and libraries. This drastically reduces effort and turnaround time for clinical trial data management teams.Key Considerations for Successful GenAI Adoption in Clinical Data Management
Along with explaining the benefits, the speakers also highlighted crucial considerations and challenges when implementing GenAI and ML in clinical data management. Ensuring compliance of GenAI with regional and international regulations, comprehensive risk assessment of using the technology in clinical data management and the ability to scale and integrate this technology across functions and clinical trials are the basic considerations to follow while deploying GenAI. Beyond these, speakers touched upon key considerations when adopting AI in clinical data management:
Human-in-the-Loop Validation : While automation can generate eCRFs, human oversight remains critical. Stakeholders such as data managers, programmers, and medical writers must validate generated outputs to ensure accuracy and compliance.
Handling Protocol Variances: GenAI works optimally when protocols are structured and follow Clinical Data Interchange Standards Consortium (CDISC) standards. Deviations can limit the tool’s ability to generate trial-specific CRFs, emphasizing the importance of standardized trial documentation.
Reduced Timelines and Faster Decisions with GenAI and Automation in Clinical Trials
One of the more striking insights from the research highlights a growing shift in clinical trial data management strategies. Over 60% of surveyed companies indicated that access to advanced technologies, particularly AI in clinical data management, is a key driver behind their decision to outsource. By partnering with specialized providers, organizations can accelerate automation in clinical trials, gain access to cutting-edge Generative AI in clinical trials, and leverage deep domain expertise. This not only speeds up implementation but also delivers significant cost efficiencies while enhancing data accuracy and regulatory readiness.
Today, Generative AI in clinical trials enables the creation of robust eCRFs in as little as 3 weeks—compared to 12–16 weeks previously—through digital libraries and NLP capabilities. These advances allow users to organize protocols by trial phase, design, and more, enabling faster decisions and efficient planning.
Want to learn how leading pharmaceutical companies are using GenAI-powered solutions to optimize electronic data capture and drive transformation across their clinical R&D pipeline? Reach out to us with your requirements—we’ll be glad to connect you with our SMEs!
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