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Revolutionizing Clinical Trials: Harnessing Generative AI and ML for Enhanced Clinical Data Management

05 Feb 2024
Role of Digital Technologies and Automation in Streamlining Clinical Data Management
Conducting clinical trials is inherently expensive, labor-intensive and highly regulated. The success of clinical trials hinges on three key factors: disciplined and precise planning of clinical trials design, realistic enrolment timelines for clinical trials participants, and adherence to processes. When planning a new clinical trial, it is crucial to review past trial designs and clinical trials data to understand critical success factors and implement new learnings. However, currently, this process is challenging and time-consuming due to its manual nature and the nascent adoption of digitalized libraries. Moreover, the current manual methods of document preparation result in the generation of numerous documents. Compliance with international regulations further compounds this challenge, as these documents undergo rigorous scrutiny.
Novel digital technologies, automation tools and omnichannel outreach initiatives, that leverage Real World Data (RWD), have reduced the overall time and cost involved in designing and executing clinical trials. However, the potential impact of GenAI on clinical trials is transformational. A recent report by BCG states that by using GenAI along with 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 some compelling use cases of GenAI that can significantly improve the productivity of clinical trial data management activities.
While walking through the audience in detail regarding the use cases, they highlighted the following key benefits of using GenAI in Clinical Data Management:
Build and utilize digital libraries: Clinical trial documents are highly structured and standardized, which makes them ideal candidates for automation. By leveraging Natural Language Processing (NLP), users can create a digital library that provides an overview of each protocol section, based on the selected criteria and sort protocols by factors such as trial phase and design.  
Improve eCRF creation: Users can automate reading and understanding protocols and generate forms based on existing libraries and efficiently create electronic case report forms (eCRFs) . Versioning capabilities and the ability to maintain audit trails of edits further eases protocol amendments. In addition, eCRFs can be generated in table format like how they appear on EDC platforms enhancing readability and analysis
Streamline data extraction: Screening and extracting relevant information from the protocol, is one of the crucial tasks in clinical data management. With GenAI, users can create an entire study, including eCRFs that is adaptable to various EDC systems, by extracting relevant information from the protocol. By improving data extraction from historic protocols, digital libraries can simplify protocol design by facilitating search and analysis. Downloadable data from these libraries provides quick access to sorted and analyzed information helping users to identify and match concepts in the protocol to existing libraries, ensuring efficient document creation
Improve data management: Users can streamline documentation process by creating data management plan documents, swiftly edit check validation specifications, and test cases for the documents that were automatically generated, leveraging the existing standards and libraries.
Key considerations to ensure success of Generative AI 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 cited that the below two key considerations also play a critical role in the success of GenAI in Clinical Data Management:
Human-in-the-Loop Validation: While eCRFs can be automatically generated, there is a crucial emphasis on human validation. The generated forms must be reviewed and approved by stakeholders such as data managers, programmers, medical writers, biostatisticians, or clinical operations teams to ensure compliance with the protocol.
Handling Protocol Variances: GenAI enables analysis of Clinical Data Interchange Standards Consortium (CDISC) standards and generates basic eCRFs. However, if the protocol deviates from existing standards or lacks trial-specific CRFs, it can be difficult to generate required forms. This highlights the importance of structured and standardized protocols for optimal performance of GenAI.
Reduced timelines and faster decision making are the pillars that will drive implementation of Generative AI in Clinical Data Management
Currently, analyzing previous trial designs, understanding key success factors and integrating new insights is arduous and time consuming due to the absence of comprehensive digital libraries. Adoption of GenAI shows promising results in reducing the time required to effectively create robust eCRFs due to which studies can go live in just 3 weeks as against 12-16 weeks using traditional approaches. With GenAI and NLP users can create a digital library that offers an overview of each protocol section, customizable based on user-defined criteria. These resources can empower them to organize protocols by trial phase, design, and other relevant factors and create effective study designs facilitating efficient document creation and decision-making in clinical trial planning.
The complete webinar can be accessed here.
Want to know more about how pharmaceutical companies are leveraging our GenAI and ML enabled solutions to drive efficiencies in clinical data management and overall clinical R&D function? Share your requirements with us and we would be happy to connect you with our SMEs!

Authors

Jhansi R Bijay
Jhansi R Bijay

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