Comparative Study of ETL Tools for Transforming Healthcare Data to the OMOP Common Data Model (CDM)
Adnan Jouned1, Heike Düsseldorf1, Florian Katsch1,2
1Institute of Medical Information Management, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Studies in Health Technology and Informatics
|May 17, 2025
Summary
Standardizing complex healthcare data is crucial for research. This study found Pentaho, Apache NiFi, and custom tools effectively transform data into the OMOP Common Data Model (CDM), with varying usability for different user expertise levels.
Area of Science:
- Health Informatics
- Data Science
- Biomedical Research
Background:
- Healthcare data complexity necessitates standardization for research.
- The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) facilitates consistent data analysis.
- Efficient Extract, Transform, Load (ETL) processes are vital for data standardization.
Purpose of the Study:
- To evaluate the suitability of Pentaho, Apache NiFi, and a custom ETL tool for transforming healthcare data into the OMOP CDM.
- To compare these tools based on criteria including connectivity, usability, performance, and flexibility.
- To provide insights for selecting appropriate ETL tools for OMOP CDM data transformation.
Main Methods:
- Literature review to establish evaluation criteria for ETL tools.
- Comparative analysis of Pentaho, Apache NiFi, and a custom-built ETL tool.
- Assessment based on connectivity, interoperability, user interface, performance, and technical flexibility.
Main Results:
- All evaluated ETL tools (Pentaho, Apache NiFi, custom) are adequate for basic OMOP CDM data transformation.
- Pentaho offers superior ease of use, benefiting non-expert users.
- Apache NiFi is more suitable for advanced users, while custom tools provide flexibility at the cost of development effort.
Conclusions:
- The choice of ETL tool for OMOP CDM transformation depends on user expertise and project requirements.
- Pentaho, Apache NiFi, and custom solutions offer viable options for standardizing healthcare data.
- Effective ETL tool selection can enhance the efficiency and reproducibility of health research.
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