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This review of clinical data warehouse (CDW) methods highlights a need for more research in CDW design and governance. Further multi-site studies are essential for advancing CDW maturity in healthcare organizations.

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Area of Science:

  • Health Informatics
  • Data Management
  • Clinical Research Informatics

Background:

  • Clinical Data Warehouses (CDWs) integrate diverse data for decision-making and secondary use in healthcare organizations (HCOs).
  • Limited scientific literature exists on CDW methods despite significant HCO efforts.
  • Existing literature often lacks focus on critical areas like data integration, ETL optimization, and governance.

Purpose of the Study:

  • To systematically review and characterize current CDW methods in HCOs.
  • To identify evidence-based recommendations for CDW practices.
  • To guide the advancement of CDW design, implementation, and utilization.

Main Methods:

  • A systematic PubMed search identified 137 articles published between 2011 and 2021.
  • Abstracts were screened by two authors; full-text articles were independently reviewed and abstracted.
  • Discrepancies were resolved through consensus to ensure data accuracy.

Main Results:

  • Analysis revealed 137 relevant articles from 55 journals and 3 conference proceedings.
  • Key areas needing increased focus include CDW design (data integration, ETL, data quality, semantic representation) and governance (support tools, data literacy, structure, financial models).

Conclusions:

  • The study identifies developed CDW topics and areas requiring further attention, bridging general data management practices and research data needs.
  • More multi-site and multi-aspect studies are recommended to enhance CDW maturity.
  • Improved reporting on CDW design and governance is crucial for advancing the field.