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Beyond Cohort Selection: An Analytics-Enabled i2b2.

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This study enhances the i2b2 software, a clinical data tool, by adding statistical analysis capabilities. This empowers researchers to accelerate hypothesis testing and data-driven clinical research.

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Area of Science:

  • Biomedical Informatics
  • Clinical Research Informatics
  • Health Data Science

Background:

  • The i2b2 software is a popular platform for secondary use of clinical data, primarily for cohort identification.
  • Current i2b2 functionalities lack integrated data analysis tools, hindering hypothesis testing efficiency.
  • Clinical researchers require robust analytical capabilities within their data management platforms.

Purpose of the Study:

  • To extend the i2b2 framework with advanced statistical analysis functionalities.
  • To enable clinical researchers to perform statistical analyses directly within i2b2, accelerating hypothesis testing.
  • To create a flexible and user-friendly extension for secondary clinical data analysis.

Main Methods:

  • Developed a flexible i2b2 extension capable of integrating with various statistical engines.
  • Implemented initial applications for basic statistical analyses and survival analysis.
  • Designed user interfaces with a focus on usability for clinical researchers.

Main Results:

  • Successfully extended the i2b2 framework to incorporate statistical analysis capabilities.
  • Demonstrated the feasibility of performing basic and survival analyses through the new extension.
  • User interfaces were developed to ensure ease of access and use for researchers.

Conclusions:

  • The developed i2b2 extension significantly enhances the platform's utility for clinical research.
  • Empowering i2b2 with statistical analysis tools accelerates hypothesis testing and data interpretation.
  • The extension provides a more integrated and efficient workflow for clinical data analysis.