TCS launches AI Platform for clinical trial oversight

Tata Consultancy Services (TCS) on Monday launched the next generation of its integrated platform for oversight across the clinical trial lifecycle. Called TCS ADD Risk-Based Quality Management (RBQM), which has a suite of  AI-powered modules designed to equip pharmaceutical and MedTech companies, personal care sponsors, CROs (Clinical Research Organization), and research institutes.

“ The platform’s AI and advanced analytics capabilities enable our clients to make data-driven decisions, optimize resource allocation, and ultimately bring life-changing therapies to patients faster and more efficiently. This version represents our most comprehensive vision yet for RBQM,” said Rachna Malik, Global Head, TCS ADD.

The current version has four new modules for risk assessment and categorisation, quality tolerance limit, clinical trial analytics, and subject data analytics.

The integrated Risk Assessment and Categorization Tool (RACT) module with smart workflows ensures documentation and approvals in alignment with industry standards. The Quality Tolerance Limit (QTL)module is an AI-based statistical tolerance analytics tool powered by smart data input and a comprehensive and extendable QTL library. The Clinical Trial Analytics module offers advanced analytics with proprietary AI algorithms to identify snags in real-time, monitor trials, and track site performance. Lastly, the Subject Data Analytics module enables centralised statistical monitoring of subject data.

RBQM leverages AI, ML, and advanced analytics to provide real-time risk monitoring, risk assessment and categorisation, predictive insights, and automated workflows. This approach aims to address the growing complexity in monitoring and managing clinical trials in the modern research landscape and aligns with the latest regulatory guidelines. The platform also integrates Quality by Design (QbD) principles from study design through execution, enabling real-time connection between protocol risks, monitoring priorities, and data signals.