REDCap Data Quality Module

REDCap includes Data Quality tools that help identify potential problems in project data. These tools can run predefined rules and custom rules to detect missing values, invalid values, field validation errors, duplicate records, and logic problems. While the exact features available may depend on institutional configuration, the Data Quality module is an important starting point for routine review. Predefined REDCap rules may identify blank values, invalid values, outliers, or duplicate records. Custom rules allow the data manager to define study-specific logic. For example, a custom rule might identify records where discharge date is before admission date, day 28 follow-up date is outside the allowed visit window, malaria test result is missing for enrolled participants, or pregnancy status is recorded for participants for whom the field should not apply. Data Quality rules should be aligned with the data quality plan. It is not enough to run rules occasionally without a response process. The study team should decide how often rules are run, who reviews the output, which findings become queries, and how resolved issues are documented. For example, a weekly quality review may generate a list of missing day 28 outcomes that site coordinators must resolve within seven days. REDCap reports complement Data Quality rules. Reports can show missing forms, incomplete records, open queries, enrollment by site, visit completion, unresolved adverse events, or delayed data entry. Reports are often more understandable to site users than raw rule output. A well-designed set of reports can support both central monitoring and site self-management. For larger studies, REDCap checks may be supplemented by R scripts. R can perform more complex checks, summarize trends, visualize missingness, detect unusual site patterns, and generate automated quality reports. The course will introduce R-based quality checking in later chapters, but learners should understand that REDCap and R can work together. REDCap provides structured capture and immediate validation; R supports reproducible, flexible, and scalable review. **Table 5.4: Examples of REDCap Data Quality Checks**