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Pointers to Navigate Between a Graph Database and a Clinical Data Warehouse
Julien Ehrsam1,2, Christophe Gaudet-Blavignac1,2, Cyrille Duret1
1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Geneva University Hospitals uses graph databases to represent complex healthcare data semantically, enabling patient information discovery without storing sensitive data. Pointers link graph data to the clinical data warehouse for full data retrieval.
Area of Science:
- Health Informatics
- Database Management
- Semantic Web Technologies
Background:
- Growing use of graph databases for complex healthcare data.
- Need for secure and comprehensive patient data analysis.
- Limitations of traditional databases in representing complex relationships.
Purpose of the Study:
- To describe a novel approach using graph databases for healthcare data.
- To enable patient-level information discovery without direct data storage.
- To establish a secure method for linking graph representations to source data.
Main Methods:
- Implementation of a graph database at Geneva University Hospitals.
- Utilizing semantic standards for data representation.
- Employing JMESPath query language for data linking.
Main Results:
- A graph database representation of patient data was successfully created.
- Full patient-level information view is achievable for clinical questions and discovery.
- Sensitive patient data is not stored directly in the graph database.
- Pointers effectively link graph expressions to the clinical data warehouse.
Conclusions:
- Graph databases offer a secure and effective method for managing complex healthcare data.
- Semantic representation allows for comprehensive data insights without compromising privacy.
- JMESPath provides a robust mechanism for integrating graph data with clinical data warehouses.
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