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Longitudinal Studies01:26

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Implementing a Data Management Platform for Longitudinal Health Research.

Jan-Patrick Weiß1, Ursula Hübner1, Jens Rauch1

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|September 9, 2017
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Summary

This study developed a system to analyze longitudinal health IT adoption data, integrating diverse datasets for robust statistical analysis. The system successfully combined survey and secondary data from multiple years.

Keywords:
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Area of Science:

  • Health Informatics
  • Information Systems Research
  • Diffusion of Innovation Theory

Background:

  • Health IT adoption research often relies on longitudinal analyses but frequently uses cross-sectional designs.
  • Existing methodologies may limit the comprehensive analysis of evolving health IT trends over time.

Purpose of the Study:

  • To design and implement a system for consolidating, integrating, and analyzing longitudinal health IT adoption survey data.
  • To overcome limitations of cross-sectional studies by enabling robust analysis of data across multiple years.
  • To facilitate the integration of heterogeneous datasets and secondary data sources.

Main Methods:

  • A 5-tier architecture was implemented, including tiers for data capture, representation, logic, presentation, and integration.
  • A data vault schema was utilized for effective data historization and separation of storage from analytics.
  • Data analysis involved compiling data into data marts and utilizing R and associated tools.

Main Results:

  • The system successfully consolidated and analyzed health IT report data from 2011, 2013, and 2017.
  • Heterogeneous datasets were flexibly integrated, allowing for the selection of comparable items.
  • Longitudinal analysis was enabled by combining survey data with secondary longitudinal data.

Conclusions:

  • The developed system provides a robust framework for longitudinal analysis in health IT adoption research.
  • This approach enhances the ability to study IT trends and impacts over extended periods.
  • The system facilitates more comprehensive and flexible data integration and analysis for future research.