AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows
Summary: arXiv:2602.00052v2 Announce Type: replace-cross
Abstract: Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial teams. Structuring protocol content into standard formats has the potential to improve efficiency, support documentation quality, and strengthen compliance. We evaluate an Artificial Intelligence (AI) system using generative LLMs with Retrieval-Augmented Generation (RAG) for automated clinical trial protocol information extraction. We compare the extraction accuracy of our clinical-trial-specific RAG process against that of publicly available (standalone) LLMs. We also assess the operational impact of AI-assistance on simulated extraction Clinical Research Coordinator (CRC) workflows. Our RAG process shows higher extraction accuracy (89.0%) than standalone LLMs with fine-tuned prompts (62.6%) against expert-supported reference annotations. In simulated extraction workflows, AI-assisted tasks are completed 40% faster, are rated as less cognitively demanding and are strongly preferred by users. While expert oversight remains essential, this suggests that AI-assisted extraction can enable protocol intelligence at scale, motivating the integration of similar methodologies into real-world clinical workflows to further validate its impact on feasibility, study start-up, and post-activation monitoring.
Introduction
The landscape of clinical trials is becoming increasingly intricate, with protocols that are often amended and revised to accommodate new insights and regulatory demands. This complexity places a burden on clinical trial teams who must efficiently manage and extract vital information from these documents.
Methodology
To address these challenges, we implemented an AI system that leverages generative LLMs integrated with a Retrieval-Augmented Generation (RAG) framework. The primary objective was to automate the extraction of critical information from clinical trial protocols, thereby enhancing efficiency and accuracy.
Results
Our findings reveal that the RAG process significantly outperforms standalone LLMs in terms of extraction accuracy:
- Extraction Accuracy: RAG process achieved 89.0% accuracy.
- Standalone LLM Accuracy: Achieved 62.6% accuracy with fine-tuned prompts.
These results were benchmarked against expert-supported reference annotations, highlighting the effectiveness of our approach in extracting protocol information.
Operational Impact
We further assessed the operational impact of AI assistance on Clinical Research Coordinator (CRC) workflows through simulated extraction tasks. The results indicated:
- Task Completion Speed: AI-assisted tasks were completed 40% faster than traditional methods.
- Cognitive Demand: Tasks were rated as less cognitively demanding by participants.
- User Preference: Participants expressed a strong preference for the AI-assisted approach over conventional methods.
Conclusion
While human oversight remains a critical component of clinical trial workflows, our study suggests that AI-assisted extraction can significantly enhance protocol intelligence at scale. By integrating such methodologies into real-world clinical settings, we can further validate their impact on trial feasibility, study start-up timelines, and post-activation monitoring, ultimately leading to improved outcomes in clinical research.