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Structured Chaos: Unintended Consequences of Over-Modeling
Christophe Gaudet-Blavignac1,2, Julien Ehrsam1,2, Adel Bensahla Talet1,2
1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Structuring electronic health data faces an interoperability paradox. Robust data governance, versioning, and semantic standardization, not new models, are key to resolving semantic failures in clinical data elements.
Area of Science:
- Health Informatics
- Data Science
- Clinical Data Management
Background:
- Multiple models for structuring electronic health data have led to an interoperability paradox.
- Semantic failures hinder the effective use of aggregated clinical data elements.
Purpose of the Study:
- To identify the root causes of semantic failure in structured electronic health data.
- To propose solutions for improving data interoperability and semantic consistency.
Main Methods:
- Analysis of over 140,000 aggregated clinical data elements.
- Examination of challenges including semantic drift, lack of traceability, and loss of context.
Main Results:
- Semantic drift from non-versioned dictionaries is a key challenge.
- Missing traceability from attribute modification contributes to data inconsistency.
- Loss of context due to absent form layout in data warehouses exacerbates semantic failure.
Conclusions:
- The creation of new models is not the solution to the interoperability paradox.
- Implementing robust data governance is essential for semantic standardization.
- Mandated versioning and semantic-centered approaches are crucial for resolving semantic failures in electronic health data.
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