A Time-Aware Multi-Agentic Framework for Proactive Clinical Trial Risk Monitoring and Adaptive Decision Optimization

Abstract

Clinical trials generate large volumes of heterogeneous clinical and operational data originating from electronic data capture systems, laboratory information systems, site performance metrics, and monitoring activities. Despite advances in digital trial management, these data streams often remain isolated, resulting in delayed detection of data quality issues, limited operational transparency, and increased reliance on manual review processes, which are insufficient for handling time-aware trial complexity. This study addresses the research gap in designing an integrated, AI-driven clinical trial intelligence framework capable of time-aware data harmonization, proactive anomaly detection, and context-aware decision support. We propose a unified architecture that combines multimodal data ingestion, advanced analytics, and Agentic AI to dynamic risk adoption trial operations and enhance data quality oversight. The proposed methodology leverages artificial intelligence-based data quality assessment, predictive operational analytics, and AI agents to generate actionable insights and recommendations throughout the trial lifecycle. We utilized historical clinical trial records from the Aggregate Analysis of ClinicalTrials.gov (AACT) database, including enrolment ratio, recruitment status, study phase, sponsor category, protocol amendment frequency, study duration, and site characteristics, to construct a multi-dimensional trial risk representation. The proposed framework has integrated with multiple agents such as Risk Assessment Agent, Trial Management Agent, Prioritization Agent, and Recommendation Agent collectively used for risk identification and intervention planning.   The proposed Agentic-AI framework mechanisms to enhance early risk identification and operational intelligence. The framework was validated using multi-fold rolling time-series cross-validation and trained on historical observations to prevent temporal leakage.  The performance was evaluated using several metrics, and the proposed framework was further compared across Statistical Process Control (SPC), Logistic Regression, and Gradient Boosting-based monitoring approaches. The Experimental results show that the proposed framework illustrated optimised performance against baseline models across various predictive and operational metrics achieving accuracy (≈88), F1-score (≈88.5), and AU-ROC (≈96.1) with enhanced risk prioritisation accuracy, trial delay detection capability, and early risk identification performance. The proposed work contributes an adaptive and scalable framework for intelligent clinical trial monitoring by integrating predictive analytics with agentic decision mechanisms, time-aware validation strategies, and an intelligent fusion layer. The study advances existing clinical trial intelligence systems from reactive and static monitoring strategies towards stable, operationally optimised AI-assisted trial management.