Reinventing the Clinical Trial Workflow: TMF's Agency: Leveraging Agent-based AI Models and 'Expert Loop' to Enhance TMF's Speed, Efficiency, and Quality

Harnessing agentic AI models and ‘experts-in-the-loop’ to YOGESH TAMBE, Senior Director of Product Management at IQVIA Part 4 of a 4-part series highlighting key innovations in clinical research annually, potentially escalating to millions for a trialrunning several years). At the same time, a TMFmust ensure impeccable compliance with ethical andregulatory requirements and Good Clinical Practice; Reshaping TMF workflowsto boost speed, efficiency Like the rest of our world, the clinical trial ecosystemis being revolutionized by stunning advancementsin artificial intelligence. Much of the recent focus has Unlike standard AI automation, which generally refers tothe automation and/or machine learning of predefined,rule-based tasks, agentification integrates autonomous Therefore, as one would imagine, properly maintainingan up-to-date TMF typically involves highly manual,burdensome and cumbersome processes that arelabor-intensive and repetitive — further compoundedby the complexity caused by the lack of document In this fourth and final part of our series highlightingkey innovations in clinical research, we’ll touch uponone real-world example — of which there are many — As we have emphasized throughout this series, successfulagentification hinges on knowing precisely when andwhere to apply it, how to safeguard against errors,and where to preserve the indispensable contributions “Properly maintaining an up-to-dateTMF typically involves highly manual,burdensome and cumbersome Prudent application ofagentification to Trial Master Because the Trial Master File serves as thecomprehensive repository for all trial documentation,agentification can provide several opportunities to organization, quality control and compliance verification— enabling scalable, consistent and efficient TMF The paradigm shift: TMFs as Traditionally, sponsors focused on three core categoriesof KPIs for Trial Master Files — timeliness, completenessand quality — while striving to reduce process stepsand eliminate non-value-adding activities. Yet a newparadigm has emerged, as the wealth of documentationand data provided by TMFs represent a potentialtreasure trove for generating actionable insightsto improve current and future of trials, operationalprocesses and patient health. While the primaryTMF objectives within this shifting paradigm remain It is worth noting that agentification is set to significantlyimpact faster startup timelines in the near future. Forexample, IQVIA has utilized AI/ML models in its eTMF These agents automatically detect, prioritize and promotesuch documents in queue — for instance, IQVIA’s QC AIagents have achieved notable progress in conforming “Agentification of AI-enabled TMFprocesses and management presentsa transformative opportunity for Unlocking TMF agentificationpotential — and mitigating As mentioned at the outset, agentification of AI-enabledTMF processes and management presents a significantopportunity for sponsors to achieve unprecedented Looking ahead, agentification will accelerate studystartup even further through its advanced predictive objectives into manageable steps, and directing acoordinated ensemble of specialized agents and sub- Conclusion As agentification takes hold across clinical trials,targeted applications, specialized expertise and expert-in-the-loop safeguards will be critical to success — This synchronized collaboration maximizes agenticAI’s capabilities in reasoning, adaptation, contextualunderstanding, and forward-thinking, while maintaining Be sure to stay tuned, as we at IQVIA continue to sharenovel advancements in agentic AI and other emerginginnovations — with the ultimate goal of bringing Ultimately, our open-architecture design eliminatesthe need for sponsors to maintain inefficient “hiddenfactories” — the unseen parallel operations of rework, References 1.Clinical Data Interchange Standards Consortium. Trial Master File Reference Model. CDISC. Updated August15, 2023. Accessed February 15, 2026.https://www.cdisc.org/standards/trial-master-file-reference-model. CONTACT US iqvia.com/rds