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Proposed Schema Extensions and ETL Pathways for Integrating Wearable and Patient-Reported PGHD into OMOP-CDM for
Somayeh Abedian1,2, Rada Hussein1
1Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces an OMOP Common Data Model (OMOP-CDM) extension to effectively integrate patient-generated health data (PGHD) from wearable sensors and patient-reported outcomes (PROs) into observational research.
Area of Science:
- Health Informatics
- Biomedical Data Science
- Wearable Technology Research
Background:
- Patient-generated health data (PGHD) from wearables and patient-reported outcomes (PROs) are rapidly expanding.
- Existing data models like the OMOP Common Data Model (OMOP-CDM) lack native support for key PGHD characteristics (e.g., device provenance, calibration, temporal resolution, contextual metadata).
- This gap hinders the secondary analysis of rich, high-frequency PGHD.
Purpose of the Study:
- To propose an extension layer for the OMOP-CDM to enhance its capacity for handling heterogeneous, time-series, and PRO data.
- To develop a direct extract-transform-load (ETL) pathway compatible with the proposed OMOP-CDM extension.
- To ensure semantic consistency and maintain compatibility with existing OHDSI analytical tools and FAIR principles.
Main Methods:
- Developed an extension layer for the OMOP Common Data Model (OMOP-CDM).
- Implemented a direct extract-transform-load (ETL) pathway for integrating patient-generated health data (PGHD) and patient-reported outcomes (PROs).
- Ensured the approach preserves device traceability, allows flexible data compression, and links PRO instruments with OMOP constructs without altering the core schema.
Main Results:
- The proposed OMOP-CDM extension effectively accommodates heterogeneous, time-series, and PRO data.
- Device traceability and flexible data compression are preserved.
- The solution maintains semantic consistency and full compatibility with OHDSI analytical tools.
- Alignment with FAIR data principles is achieved.
Conclusions:
- The developed OMOP-CDM extension and ETL pathway significantly enhance the model's capability to incorporate patient-generated health data (PGHD).
- This approach facilitates the secondary analysis of wearable sensor data and patient-reported outcomes (PROs) within a standardized framework.
- The model offers a pathway for future OMOP updates and community discussion on managing PGHD.
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