IU REDCap Day 2024

1:10 pm - 1:35 pm     Rm 307     Beginner
Evolution of a REDCap clinical database, utilized for quality improvement in a statewide primary care learning collaborative across health systems, will be reviewed. Quality assurance methods and successes and challenges in standardization across a statewide QI project will be described.

ABSTRACT
Paxton | Leveraging REDCap to maintain standardization and quality assurance of data across a statewide quality improvement initiative 

Background: An interdisciplinary team of faculty in Department of Pediatrics at Indiana University School of Medicine (IUSM) launched the Early Autism Evaluation (EAE) Hub system, an innovative tiered system of developmental screening and autism evaluation within the primary care setting. The goal of this initiative is to improve localized access to early autism diagnosis for young children to promote entry into evidence-based intervention at the earliest time. Quality improvement (QI) data for every evaluation across the EAE system is collected in REDCap and used to improve care.

Objectives: Review the evolution of a REDCap clinical database utilized for quality improvement in a statewide primary care learning collaborative across health systems. Explain quality assurance methods and successes and challenges in standardization across a statewide QI project.

Methods/Results: As our program was initially implemented and then evolved with clinical application, areas for potential quality improvement were identified and measures were created and edited. Defining all these measures clearly was critical. REDCap made it easy to create data collection instruments with clear definitions. REDCap features including drop downs, date validation, and calculations that make it easy for users to enter data with greater accuracy and efficiency.

Upon trialing data collection, measure definitions were further edited based on participant feedback for better clarity. REDCap’s data dictionary, codebook and logging feature make it simple to track these edits for future analysis. When measures are edited, REDCap allows you to hide the older fields/questions so older data is not lost. Older choices can also be hidden.

Accuracy of data entry is essential. Training data entry personnel is a critical step in maintaining the integrity of the data. New personnel receive virtual training by our team’s data manager that includes the most current data definitions and guidance for locating each measure. Questions and feedback are encouraged. Timely data entry is also important. If data entry is late (over 1 month), teams receive a reminder email to enter their data.

Even with all these quality assurance methods, data must still be cleaned and verified prior to analysis. Any outliers are confirmed with the EAE Hub sites. If edits are needed, it is important to edit the original record in REDCap for future analysis. To further ensure quality assurance, EAE Learning Collaborative virtual meetings are held monthly. During these 40 minutes calls, data summaries are shared and discussions are held regarding measure clarification and quality improvement projects are planned and implemented.

Conclusions: Using REDCap for the EAE statewide network data collection improves accuracy, efficiency and clarity of data entry and data management. By analyzing QI data collected in REDCap, standardization to our EAE model can be verified, opportunities for process and care improvement can be realized and information can be disseminated to collaborators and funders.