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ETL Application with R Shiny Visualization.

Max Bergmann1, Dennis Hübner1, Antonia Ewald1

  • 1Data Integration Center, Medical University Lausitz - Carl Thiem, Cottbus, Germany.

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PubMed
Summary
This summary is machine-generated.

We developed a flexible healthcare data pipeline framework with automated validation. This system, enhanced by an R/Shiny app, significantly speeds up data processing and error detection in clinical research.

Keywords:
Data Integration CenterData ProcessingData QualityETLVisualization

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Area of Science:

  • Health Informatics
  • Data Engineering
  • Clinical Research Informatics

Background:

  • Healthcare data is complex and fragmented, posing challenges for efficient processing and analysis.
  • Existing data pipelines often lack flexibility and robust error-checking mechanisms, leading to delays and data quality issues.
  • The need for transparent and reliable data management is critical in clinical research.

Purpose of the Study:

  • To introduce a modular ETL (Extract, Transform, Load) framework specifically designed for the healthcare domain.
  • To enhance data pipeline efficiency and correctness through automated validation and concurrency control.
  • To provide real-time monitoring and visualization capabilities for complex data workflows.

Main Methods:

  • Developed a modular ETL framework supporting heterogeneous data sources.
  • Integrated an interactive R/Shiny application for real-time monitoring and performance visualization.
  • Implemented automated data validation and concurrency control mechanisms.
  • Benchmarked the new framework against traditional linear workflows.

Main Results:

  • The modular ETL framework demonstrated increased flexibility across diverse healthcare data sources.
  • Automated validation and concurrency control ensured robust data correctness.
  • The R/Shiny application provided real-time monitoring, error exploration, and performance visualization.
  • Benchmarking showed a 1.59x reduction in processing time and a 2.8x reduction in error detection time compared to legacy systems.

Conclusions:

  • The proposed ETL framework significantly improves operational transparency and data quality assurance in clinical research.
  • The integration of an R/Shiny dashboard enhances the management and understanding of complex parallel data pipelines.
  • This solution offers a scalable and efficient approach to healthcare data integration and analysis.