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Development of a public health reporting data warehouse: lessons learned
Seyed Ali Mussavi Rizi1, Abdul Roudsari
1School of Health Information Science, University of Victoria, Victoria, Canada.
Studies in Health Technology and Informatics
|August 8, 2013
Summary
This study highlights key factors for successful enterprise data warehouse implementation in public health surveillance. An iterative approach, robust data integration, and strong management support were crucial for overcoming challenges.
Area of Science:
- Health Informatics
- Public Health Surveillance
- Data Management
Background:
- Data warehouse projects face significant organizational and technical risks.
- Enterprise-level data warehouses require iterative implementation for successful evaluation.
- Public health surveillance systems generate complex data requiring robust management.
Purpose of the Study:
- To describe lessons learned from the successful development of an enterprise data warehouse for public health surveillance.
- To identify critical success factors in a large-scale data warehousing project.
- To provide insights for future data warehouse implementations in healthcare settings.
Main Methods:
- An iterative and prototyping approach was employed for data warehouse development.
- Technical challenges in data extraction and integration from clinical systems were addressed.
- A novel record linkage methodology was developed and implemented.
- Flexible and reusable data modeling techniques were utilized.
Main Results:
- Successful development and implementation of the first phase of an enterprise data warehouse.
- Overcoming challenges in extracting and integrating data from large-scale clinical and ancillary systems.
- Development of a novel approach for clinical record linkage.
- Establishment of flexible and reusable clinical data models.
- Secured timely senior management support, a critical success factor.
Conclusions:
- An iterative and prototyping development approach is effective for enterprise data warehouses.
- Addressing technical challenges in data integration and employing novel record linkage are vital.
- Flexible data modeling and strong senior management support are essential for project success.
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