TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

2025-07-16

AI Summary

Clinical trial eligibility criteria design faces challenges including a vast exploration space, weak interactivity, and difficulty integrating fine-grained electronic health record (EHR) features. To address these, we propose a knowledge-driven and outcome-driven dual-path visual analytics system. The system enables clinicians to iteratively explore eligibility criteria over multidimensional patient clinical indicators—such as vital signs, laboratory test results, and temporal medication histories—and incorporates history tracking and data provenance to support coupled analysis of inclusion/exclusion criteria, patient characteristics, and clinical outcomes. Key innovations include dual-path coupled modeling, dynamic criteria–outcome mapping visualization, and interpretable decision logging. Evaluated on real-world EHR data from septic shock and sepsis-associated acute kidney injury cohorts, the system significantly improves the efficiency, scientific rigor, and transparency of eligibility criteria design.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

Problem

• Enhancing interactive exploration of clinical trial eligibility criteria
• Incorporating EHR data for refining eligibility criteria design
• Tracking criteria adjustments impact on outcomes and patient characteristics

Innovation

• Visual analytics system for criteria design
• Integrates knowledge and outcome-driven approaches
• Supports history-tracking for criteria evolution