Semantic Representation of Medical Data Collection Forms Using Standards

Data collection forms and questionnaires are widely used to capture patient data, but their development often receives insufficient attention. The RES-Q+ project aims to semantically standardise a widely used international stroke registry data collection form using interoperability standards such as SNOMED CT and HL7 FHIR, pursuing a canonical, knowledge-graph-based representation as an ultimate goal. We analysed patterns in this RES-Q data collection form to derive general principles for improving the design and semantic alignment of medical forms. We studied the way how residuals such as "none", "other" and "unknown" can be modelled using SNOMED CT codes bound to FHIR resources.

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Publication Details

Journal
Studies in health technology and informatics
Published
2025-05-15
DOI
https://doi.org/10.3233/shti250440
Citations
1
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
6.21
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article

Semantic Representation of Medical Data Collection Forms Using Standards

Catalina Martínez-Costa, Stefan Schulz
1 citations
Studies in health technology and informatics
Biomedical Text Mining and Ontologies
6.21
article

Semantic Representation of Medical Data Collection Forms Using Standards

Catalina Martínez-Costa, Stefan Schulz
article en
1 citations

Abstract

Data collection forms and questionnaires are widely used to capture patient data, but their development often receives insufficient attention. The RES-Q+ project aims to semantically standardise a widely used international stroke registry data collection form using interoperability standards such as SNOMED CT and HL7 FHIR, pursuing a canonical, knowledge-graph-based representation as an ultimate goal. We analysed patterns in this RES-Q data collection form to derive general principles for improving the design and semantic alignment of medical forms. We studied the way how residuals such as "none", "other" and "unknown" can be modelled using SNOMED CT codes bound to FHIR resources.

Studies in health technology and informaticsVol. 327
Medical University of Graz (AT), Universidad de Murcia (ES)
Openalex Percentile: Top 6%
Biomedical Text Mining and Ontologies
6.21
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