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

Customer Problem

The customer needed help extracting business insights from the wealth of clinical query data and metadata in its Veeva Electronic Data Capture (EDC) system. The information would help clinical operations teams determine the time it takes to lock a database for a study.

USDM Solution

USDM developed a holistic view of queries from EDC metadata that included:

• Query Metrics: To identify areas for improvement, USDM looked at operational measures like query turnaround times and query volume.
• Query Causes: To optimize protocols and EDC builds and to generate fewer queries, USDM evaluated query root causes by event, form, and field.
• Query Agents: To determine which users had the most impact on data cleaning, USDM focused on which site users were responsible for generating the most queries and which study team users were managing and closing the most queries.

Business Outcomes

The customer is better able to identify high-performing and low-performing sites and clinical leads are able to fine-tune their strategy to address critical issues. Data cleaning forecasting helps the customer predict the amount of work it will take to clean the database based on how long it has taken in the past. They can also predict how much data can be cleaned in a certain amount of time.