Query metrics help the study team understand the volume, type, timeliness, and resolution of data quality issues. They turn query management from a reactive process into a monitoring tool. A high number of queries from one site, one form, or one variable may indicate unclear CRF design, insufficient training, workflow problems, or source documentation gaps.
Common query metrics include number of open queries, queries opened per week, query rate per participant, average query resolution time, number of queries older than a defined threshold, queries by site, queries by form, queries by variable, and proportion of queries closed. Critical query metrics may focus on primary outcomes, eligibility, consent, adverse events, or safety variables.
Query aging is particularly important. An open query that remains unresolved for weeks may delay cleaning, analysis, or safety review. The data quality plan should define expected response timelines. For example, safety-related queries may require response within two working days, while routine data queries may require response within seven days. Queries exceeding the timeline should be escalated.
Query metrics should be interpreted carefully. A site with many queries may have poor data quality, but it may also have high enrollment volume or more active monitoring. Rates are often more informative than raw counts. For example, queries per enrolled participant or queries per completed visit may allow fairer comparison across sites. Trends over time are also useful. A site with improving query rates after retraining demonstrates learning.
Metrics should be shared constructively. Publicly shaming sites is rarely helpful. Instead, data quality meetings should use metrics to identify support needs, clarify guidance, improve forms, or adjust monitoring. Query metrics can also highlight strong performance and encourage peer learning across sites.