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Publication Detail
Methods for enhancing the reproducibility of biomedical research findings using electronic health records.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Review
  • Authors:
    Denaxas S, Direk K, Gonzalez-Izquierdo A, Pikoula M, Cakiroglu A, Moore J, Hemingway H, Smeeth L
  • Publication date:
    11/09/2017
  • Pagination:
    31
  • Journal:
    BioData mining
  • Volume:
    10
  • Medium:
    Electronic-eCollection
  • Print ISSN:
    1756-0381
  • Language:
    eng
  • Addresses:
    Farr Institute of Health Informatics Research, 222 Euston Road, London, UK.
Abstract
The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of biomedical research by the wider community. However, a substantial proportion of health research using electronic health records (EHR), data collected and generated during clinical care, is potentially not reproducible mainly due to the fact that the implementation details of most data preprocessing, cleaning, phenotyping and analysis approaches are not systematically made available or shared. With the complexity, volume and variety of electronic health record data sources made available for research steadily increasing, it is critical to ensure that scientific findings from EHR data are reproducible and replicable by researchers. Reporting guidelines, such as RECORD and STROBE, have set a solid foundation by recommending a series of items for researchers to include in their research outputs. Researchers however often lack the technical tools and methodological approaches to actuate such recommendations in an efficient and sustainable manner.In this paper, we review and propose a series of methods and tools utilized in adjunct scientific disciplines that can be used to enhance the reproducibility of research using electronic health records and enable researchers to report analytical approaches in a transparent manner. Specifically, we discuss the adoption of scientific software engineering principles and best-practices such as test-driven development, source code revision control systems, literate programming and the standardization and re-use of common data management and analytical approaches.The adoption of such approaches will enable scientists to systematically document and share EHR analytical workflows and increase the reproducibility of biomedical research using such complex data sources.
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