REDLetr: Workflow and tools to support the migration of legacy clinical data capture systems to REDCap

• Type of Material: Article
• Authors: William D. Dunn Jr., Emory University; Jake Cobb, Georgia Institute of Technology; Allan Levey, Emory University; David Gutman, Emory University
• Language: English
• Date: 2016-06-27
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016
• Final Published Version (URL): http://dx.doi.org/10.1016/j.ijmedinf.2016.06.015
• Title of Journal or Parent Work: International Journal of Medical Informatics
• ISSN: 1386-5056
• Volume: 93
• Start Page: 103
• End Page: 110
• Grant/Funding Information: Funding was provided through NIH Grants P30 NS055077 and P50 AG025688.
• Keywords: MANAGEMENT; Science & Technology; Technology; Electronic health record; Clinical research informatics; Life Sciences & Biomedicine; TRIALS; Health Care Sciences & Services; Clinical informatics; Computer Science; QUESTIONNAIRES; ETL; ELECTRONIC DATA CAPTURE; REDCap; Biomedical informatics; Computer Science, Information Systems; Medical Informatics
• Research Categories: Biology, Bioinformatics; Biology, Neuroscience

Abstract

Objective: A memory clinic at an academic medical center has relied on several ad hoc data capture systems including Microsoft Access and Excel for cognitive assessments over the last several years. However these solutions are challenging to maintain and limit the potential of hypothesis-driven or longitudinal research. REDCap, a secure web application based on PHP and MySQL, is a practical solution for improving data capture and organization. Here, we present a workflow and toolset to facilitate legacy data migration and real-time clinical research data collection into REDCap as well as challenges encountered. Materials and methods: Legacy data consisted of neuropsychological tests stored in over 4000 Excel workbooks. Functions for data extraction, norm scoring, converting to REDCap-compatible formats, accessing the REDCap API, and clinical report generation were developed and executed in Python. Results: Over 400 unique data points for each workbook were migrated and integrated into our REDCap database. Moving forward, our REDCap-based system replaces the Excel-based data collection method as well as eases the integration into the standard clinical research workflow and Electronic Health Record. Conclusion: In the age of growing data, efficient organization and storage of clinical and research data is critical for advancing research and providing efficient patient care. We believe that the workflow and tools described in this work to promote legacy data integration as well as real time data collection into REDCap ultimately facilitate these goals.