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Clinical data warehousing for evidence based decision making.

Lekha Narra1, Tony Sahama2, Peta Stapleton2

  • 1School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Queensland 4000, Australia.

Studies in Health Technology and Informatics
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Summary

Data warehousing integrates diverse health data, enabling analysis of lifestyle factors impacting obesity. This approach supports evidence-based decisions for improved health outcomes.

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Area of Science:

  • Health Informatics
  • Data Science
  • Public Health

Background:

  • Health data exists in fragmented silos, hindering evidence-based decision-making and quality outcome assessment.
  • Analyzing the complex interplay between lifestyle and health conditions like obesity is challenging with disparate data.

Purpose of the Study:

  • To demonstrate the feasibility of data warehousing for aggregating and analyzing heterogeneous health data.
  • To investigate the impact of various lifestyle factors on obesity using a unified data model.

Main Methods:

  • Developed a proof-of-concept data warehousing model for integrating disparate health datasets.
  • Employed data aggregation and analysis techniques to explore correlations between lifestyle and obesity.

Main Results:

  • Successfully aggregated and analyzed diverse health data, illustrating the potential of data warehousing.
  • Identified key lifestyle factors influencing obesity through the implemented data model.

Conclusions:

  • Data warehousing offers a viable solution for overcoming health data silos.
  • The proposed model can be adapted for studying other public health issues beyond obesity.