Building a healthcare data warehouse: considerations, opportunities, and challenges

Area of Science:

• Health Informatics
• Data Science in Healthcare
• Digital Health Transformation

Background:

• Digitalization is transforming healthcare towards data-driven, evidence-based practices.
• Healthcare data warehouses (DWs) are central to this transformation, aggregating and analyzing diverse clinical and administrative data.
• Effective DWs improve clinical decision-making, care quality, and research capabilities.

Purpose of the Study:

• To review the literature on building and implementing healthcare data warehouses.
• To introduce real-world, disease-specific applications of data warehousing in healthcare.
• To demonstrate adapting traditional DW design for national-scale digital health infrastructures, exemplified by BiotherDW.

Main Methods:

• Literature review of existing data warehouse development and implementation processes.
• Analysis of theoretical frameworks and practical healthcare applications.
• Case study of BiotherDW for national-scale digital health infrastructure.

Main Results:

• DWs provide timely access to comprehensive patient data for clinicians, enhancing decision-making and care quality.
• DWs facilitate population-level analytics, predictive modeling, and health service research for insights into disease patterns and system efficiencies.
• Successful DW implementation requires careful consideration of system architecture, data governance, interoperability, and ETL/ELT frameworks.

Conclusions:

• Data warehouses are essential infrastructure for personalized, high-quality, and cost-effective healthcare.
• DWs drive scalable research, operational efficiency, and evidence-based health policy.
• Adapting DW design, like with BiotherDW, is key for national digital health infrastructures.