Query Management Workflow
Learning Outcomes
Outcomes
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Learning Outcomes
- Explain the meaning of data quality in clinical research and why it must be managed continuously.
- Describe major dimensions of data quality, including accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity.
- Identify common sources of data quality problems across the clinical research lifecycle.
- Develop a data quality plan that defines checks, responsibilities, timelines, and escalation procedures.
- Use REDCap Data Quality tools and reports to detect missing, invalid, inconsistent, and duplicate data.
- Explain the query management workflow from discrepancy identification to query closure.
- Write clear, neutral, non-leading data queries.
- Interpret query metrics and use them to improve study operations.
- Explain central monitoring and risk-based monitoring approaches.
- Describe database freeze and database lock and the data management work required before each milestone.