Intelligent Query Monitoring Helps Identify High- and Low-Performing Clinical Sites and Users

Transforming Clinical Query Management with AI-Driven Insights

Customer Problem

Clinical trials are inherently complex, and ensuring data accuracy is a critical challenge exacerbated by high query volumes and variable performance across clinical sites. The customer, a clinical-stage pharmaceutical company, struggled to extract actionable business insights from the extensive clinical query data housed in their Veeva Electronic Data Capture (EDC) system. Key questions—such as the time required for database lock or the root causes of persistent queries—remained unanswered due to siloed data and manual processes.

USDM Solution

To address these challenges, USDM developed an AI-powered, SaaS-based solution tailored to streamline and enhance clinical query management. The solution included:

1. Holistic Query Analytics: By integrating metadata from EDC and other potential sources, the solution provided:
   • Query Metrics: Operational insights, such as query turnaround times and volume trends, for continuous improvement.
   • Query Causes: Deep-dive analysis into root causes by event, form, and field, enabling optimized protocol and EDC designs.
   • Query Agents: Identify key contributors and highlight high-performing and underperforming site users and study team members.
2. Conversational AI Capabilities: Users could gain actionable insights by asking questions like:
   • “How many unresolved queries remain?”
   • “What impact does site performance variability have on query resolution times?”
   • “Which actions will expedite the resolution of critical queries?”
3. Predictive Analytics: Forecasting tools enabled:
   • Data cleaning workload predictions based on historical trends.
   • Accurate estimations of time required to lock databases, enhancing project planning and resource allocation.
4. Actionable Intelligence: Beyond insights, the solution prioritized actions to:
   • Optimize focus areas for Clinical Research Associates (CRAs) and site teams.
   • Address staffing or training deficiencies at underperforming sites.
   • Pinpoint and address inefficiencies in query resolution workflows.

Business Outcomes

By implementing USDM’s AI-driven solution, the customer achieved transformative improvements:

• Enhanced Performance Visibility: Clinical operations teams could easily identify high- and low-performing sites, enabling targeted interventions.
• Improved Efficiency: Reducing manual data review time by 50–60%, saving 200–500 hours per study.
• Accelerated Timelines: Trial timelines decreased by 10–15%, equating to 2–6 months for multi-year studies.
• Optimized Data Cleaning: The customer could predict and manage data cleaning workloads more effectively, leading to faster database locks.
• Increased Regulatory Speed: The improvements in data accuracy and process efficiency contributed to a 15–25% acceleration in regulatory approval timelines.

Time to Value

USDM’s solution demonstrated rapid implementation, achieving full deployment within 28 days. This ensured the customer could quickly realize value and build on their success by expanding use cases, such as missing page reporting and user-level performance analytics.