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Related Experiment Video

Updated: May 21, 2025

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LinkR: An open source, low-code and collaborative data science platform for healthcare data analysis and

Boris Delange1, Benjamin Popoff2, Thibault Séité3

  • 1InterHop, Saint-Malo, France; LTSI, INSERM UMR 1099, University of Rennes 1, F-35042 Rennes, France.

International Journal of Medical Informatics
|March 23, 2025
PubMed
Summary

LinkR is a low-code platform that simplifies healthcare data analysis for researchers. It enhances data interoperability and collaboration, making big data research more accessible.

Keywords:
Clinical data warehouseData analysisData visualizationElectronic health recordsInteroperabilityLow-code data analysisOpen scienceWeb application

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Area of Science:

  • Health Informatics
  • Data Science
  • Medical Research

Background:

  • Clinical Data Warehouses (CDWs) provide valuable big data for medical research.
  • Lack of standardization in CDW data models hinders interoperability and research potential.
  • Advanced programming skills are often a barrier for healthcare professionals in data analysis.

Purpose of the Study:

  • To develop a user-friendly, low-code platform for manipulating, visualizing, and analyzing healthcare data.
  • To improve interoperability and accessibility of data from Clinical Data Warehouses.
  • To facilitate collaborative research on healthcare big data.

Main Methods:

  • Developed an open-source, low-code data science platform named LinkR.
  • The platform is based on the OMOP Common Data Model.
  • Integrated graphical tools and an advanced programming interface for data analysis, visualization, and manipulation.
  • Incorporated a Git module for streamlined collaboration.

Main Results:

  • LinkR enables study generation from multiple data sources, organizing data into individual and population sections.
  • User-friendly graphical tools allow customized data presentation for individual records.
  • Statistical analyses can be performed using both graphical and programming interfaces in the population data section.
  • Usability testing showed high user satisfaction with a median System Usability Scale score of 75.
  • The platform was successfully tested with a large OMOP database during a datathon with 12 concurrent users.

Conclusions:

  • LinkR democratizes access to Clinical Data Warehouse data through a low-code, open-science approach.
  • The platform facilitates data manipulation, analysis, and collaborative work in healthcare research.
  • LinkR addresses the need for accessible big data analytics tools in the medical field.