Data Quality Management and Query Resolution: Summary and Assessment

Data quality management is the continuous process of preventing, detecting, resolving, and learning from data quality problems. In clinical research, quality must be understood in relation to intended use, including participant safety, protocol compliance, analysis, reporting, and archival. Important dimensions include accuracy, completeness, validity, consistency, timeliness, uniqueness, integrity, and interpretability. Quality problems may arise from protocol ambiguity, poor CRF design, weak database configuration, site workflow challenges, incomplete source documentation, external data transfers, human error, or access-control weaknesses. A data quality plan defines critical data, checks, responsibilities, frequency, query workflow, escalation, and documentation. REDCap Data Quality tools and reports can detect missing, invalid, inconsistent, and duplicate data, while R scripts and dashboards can extend monitoring across larger or more complex studies. Query management is the structured workflow for resolving discrepancies. Effective queries are clear, specific, neutral, and non-leading. Query metrics help teams monitor data quality patterns, site performance, and response timelines. Central monitoring and risk-based monitoring allow study teams to focus attention on critical data, critical processes, and sites or variables showing elevated risk. Database freeze and database lock are formal milestones that require evidence that data management activities are complete and that the dataset is fit for analysis.