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An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Lena Baum1, Armin Müller2, Marco Johns2

  • 1Medical Informatics Group, Center of Health Data Science, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany. lena.baum@bih-charite.de.

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Metrics derived from clinical data warehouse logs, like those from Informatics for Integrating Biology and the Bedside (i2b2), reveal user interaction patterns. This analysis informs improvements for data engineers and researchers.

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Area of Science:

  • Biomedical Informatics
  • Health Data Science

Background:

  • Clinical data warehouses offer harmonized access to healthcare data for research.
  • The Informatics for Integrating Biology and the Bedside (i2b2) platform allows tailored data representations for specific use cases.
  • Iterative refinement of data representations requires understanding user interactions with the platform.

Purpose of the Study:

  • Develop metrics to describe user interactions with clinical data warehouses, specifically the i2b2 platform.
  • Create an interactive dashboard with visualizations to guide improvements for data engineers and stewards.

Main Methods:

  • Identified metrics across data usage dimensions (frequency, duration, functionality).
  • Extracted user query metadata from the i2b2 database schema.
  • Implemented visualizations in Python and integrated them into a Dash-based interactive dashboard.

Main Results:

  • Key metric categories include usage frequency, session duration, and feature utilization.
  • Developed a dashboard focusing on feature use, detailing query counts, popular concepts, and query complexity.
  • The developed dashboard enhances local i2b2 data warehouse platforms.

Conclusions:

  • Metadata from the i2b2 schema can yield metrics for understanding user interactions.
  • These metrics empower data engineers and stewards to identify platform strengths and weaknesses.
  • The findings support the iterative improvement of clinical data warehouse platforms for specific research needs.