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Overview
We would like to share that the TCS ADD™ paper titled "Automation of Trial Design Domains Generation using AI" was presented at the PharmaSUG 2025 US annual conference and featured on their website.
The paper discusses Trial Design Domains, which are a crucial part of the study data tabulation model (SDTM). They capture essential study design elements such as treatment arms, visits, inclusion/exclusion criteria, and trial summaries. These domains help present a structured view of clinical trial protocols, enabling faster understanding, comparison, and traceability of study design information.
Traditionally, creating Trial Design Datasets has been a manual, resource-intensive process. It involves extracting information from unstructured clinical trial protocols, documenting it, and developing custom programs to generate structured datasets. This method becomes even more complex and repetitive when protocols are amended, often requiring the entire cycle to be redone. It also presents challenges in maintaining traceability and consistency across versions.
This whitepaper proposes an AI-driven solution to automate the generation of Trial Design Domains, using natural language processing (NLP), ML, and rule-based automation.
Two approaches:
- Intermediate approach - Without using a study definitions repository, this method uses NLP and large language models to extract relevant data from the protocol document. A knowledge repository helps standardize and encode this content, while human review ensures quality. In parallel, parameter-based programs extract data from collected subject-level information.
- Long-term approach - Aligned with digital data flow, this method uses a structured study definitions repository (SDR). Protocol content is digitized and stored in the SDR, and AI models automatically generate Trial Design Datasets using CDISC-compliant rules. Manual reviews occur earlier in the process, improving accuracy and reducing downstream effort.
The framework ensures seamless handling of protocol amendments, enhanced traceability, and continuous learning through feedback loops. It brings automation, consistency, and scalability to the trial design data generation process, helping sponsors accelerate regulatory submissions while maintaining data integrity.
PharmaSUG 2025 US is a leading clinical global event for statistical programmers, data managers, and clinical researchers. This whitepaper showcases a forward-looking approach to modernizing clinical data operations.
About the authors
Image of Mayank Bhatia
Mayank Bhatia
Mayank Bhatia has over 22 years of clinical experience and is the Head of Product Strategy and Management in the TCS ADD™ Metadata Repository. His expertise spans building products and platforms, leading cross-functional teams, achieving operational excellence, and driving change for transformational programs. He has successfully led and delivered many large-scale and critical transformational programs. He holds a bachelor’s degree in computer science and is PMP-certified.
Image of Prasoon Sangwan
Prasoon Sangwan
Prasoon Sangwan is a Business consultant for the TCS ADD™ Life Sciences Platform. She has over 16 years of experience and leads major clinical transformation programs, including risk-based dynamic monitoring, central lab modernization, MDR implementation, SDTM automation, and protocol digitization initiatives. Prasoon has authored a book on SAS Grid and published multiple papers in the clinical and SAS space. She also holds a patent on a high-performance computing environment.
Image of Charusheela Thakur
Charusheela Thakur
Charusheela works as a Domain Consultant for the TCS ADD™ Platform and has been with TCS for over nine years. She has worked in the pharmaceutical industry for almost 20 years and specializes in clinical trial reporting, standardization, and analytics. She has a master’s degree in Statistics from the University of Mumbai.
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