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Author Notes:

Corresponding Author at: Department of Neurology, Emory University School of Medicine, 1639 Pierce Drive, Atlanta, GA 30322, USA. dgutman@emory.edu

All authors WD, JC, AL, and DG have made substantial contributions to all of the following: (1) the conception and design of the study and acquisition of data, (2) drafting the article or revising it critically for important intellectual content, (3) and final approval of the version to be submitted.

We would like to especially thank all of the personnel at the Emory University Neurology Clinic and ADRC for their input and assistance during the transition process described in this manuscript.

Conflicts of interest: None

Subjects:

Research Funding:

Funding was provided through NIH Grants P30 NS055077 and P50 AG025688.

Keywords:

  • Science & Technology
  • Technology
  • Life Sciences & Biomedicine
  • Computer Science, Information Systems
  • Health Care Sciences & Services
  • Medical Informatics
  • Computer Science
  • Electronic health record
  • Biomedical informatics
  • ETL
  • REDCap
  • Clinical informatics
  • Clinical research informatics
  • ELECTRONIC DATA CAPTURE
  • TRIALS
  • QUESTIONNAIRES
  • MANAGEMENT

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

Tools:

Journal Title:

International Journal of Medical Informatics

Volume:

Volume 93

Publisher:

, Pages 103-110

Type of Work:

Article | Post-print: After Peer Review

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.

Copyright information:

© 2016

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