ETL Processes for Integrating Healthcare Data - Tools and Architecture Patterns

Area of Science:

• Health Informatics
• Data Management
• Software Engineering

Background:

• Interoperability of healthcare information systems is critical for clinical care.
• Organizations face challenges selecting appropriate Extract, Transform, Load (ETL) tools and architecture patterns for data warehousing.
• Existing healthcare data systems are often disparate yet coexist.

Purpose of the Study:

• To provide an overview of current ETL tools for healthcare data integration.
• To demonstrate ETL processes for clinical data integration using various tools and architectures.
• To map data from diverse sources (e.g., MEONA, ORBIS) to interoperable standards (e.g., FHIR, openEHR).

Main Methods:

• Overview of available ETL tools for the healthcare sector.
• Development and demonstration of three distinct ETL processes.
• Utilizing different ETL tools and software architecture patterns for data mapping.

Main Results:

• Successful mapping of data from sources like MEONA and ORBIS to standards such as FHIR and openEHR.
• Demonstration of varied ETL tool and architecture pattern applications in clinical data integration.
• Empirical evidence on the effectiveness of chosen ETL strategies.

Conclusions:

• The selection of ETL tools and architecture patterns should align with specific project technical requirements.
• Strategic tool and pattern selection can significantly boost team efficiency in healthcare data integration projects.
• The study provides practical insights for improving healthcare information system interoperability.