IADR Abstract Archives
Using REDCap to Enhance Data Quality in Clinical Trials
Objectives: Clinical trials employ comprehensive quality management programs to maintain data integrity and validity. Multifaceted quality management programs must include strategies to combat subject and researcher variables that can lead to missing data. The Opioid Analgesic Reduction Study (OARS) uses REDCap to electronically capture data and has further leveraged the functionality of the system to prevent and/or assess and resolve data discrepancies as part of its quality management system. The study’s goal was to evaluate data quality using a novel approach in REDCap.
Methods: Subject data is entered directly into REDCap via study staff. Data validation, including structured data responses, branching logic, field-based stopping rules, and pop-up warning messages, has been preprogrammed to ensure appropriate data entry. System-generated alerts support clinical operations and also signal need for form completion. Color-coded form status designations in the REDCap dashboards are used to denote quality review status and signal areas that require review. REDCap queries are utilized to adjudicate discrepancies, missing data fields, and provide clarifications.
Results: For the first 579 subjects enrolled, 3399 forms were completed. Quality review identified no missing forms and only sixty-five (0.02%) forms were incomplete or required correction. Two hundred, thirty-five (235) total queries were initiated for 168 individual subjects across five clinical sites (23.4% corrections, 44.3% completions, and 32.3% clarifications; chi-square p<0.001), with an average of 11.9 days to close. There is significant difference in percentages of individual subjects with queries across sites, ranging from 20.4% to 56.6% (p<0.0001).
Conclusions: Effective use of REDCap functionality ensures the accuracy and completeness of the data reported. Targeted queries and repurposing dashboards play a vital role in data resolution for the OARS study. Identified differences between sites allow for root cause analysis and clinical trial process improvement. Leveraging system features has proven beneficial and has potential value in other clinical trials using REDCap.
Methods: Subject data is entered directly into REDCap via study staff. Data validation, including structured data responses, branching logic, field-based stopping rules, and pop-up warning messages, has been preprogrammed to ensure appropriate data entry. System-generated alerts support clinical operations and also signal need for form completion. Color-coded form status designations in the REDCap dashboards are used to denote quality review status and signal areas that require review. REDCap queries are utilized to adjudicate discrepancies, missing data fields, and provide clarifications.
Results: For the first 579 subjects enrolled, 3399 forms were completed. Quality review identified no missing forms and only sixty-five (0.02%) forms were incomplete or required correction. Two hundred, thirty-five (235) total queries were initiated for 168 individual subjects across five clinical sites (23.4% corrections, 44.3% completions, and 32.3% clarifications; chi-square p<0.001), with an average of 11.9 days to close. There is significant difference in percentages of individual subjects with queries across sites, ranging from 20.4% to 56.6% (p<0.0001).
Conclusions: Effective use of REDCap functionality ensures the accuracy and completeness of the data reported. Targeted queries and repurposing dashboards play a vital role in data resolution for the OARS study. Identified differences between sites allow for root cause analysis and clinical trial process improvement. Leveraging system features has proven beneficial and has potential value in other clinical trials using REDCap.