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Semantic Representation of Medical Data Collection Forms Using Standards.

Stefan Schulz¹, Catalina Martínez Costa²

¹Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.

Studies in Health Technology and Informatics

|May 17, 2025

Summary

This summary is machine-generated.

This study enhances medical data collection forms by using SNOMED CT and HL7 FHIR standards for semantic standardization. It provides principles for better form design and modeling residual data like "none" or "unknown".

Keywords:

FHIR SNOMED CT Stroke Terminology binding

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Area of Science:

Medical Informatics
Health Data Standards
Clinical Data Management

Background:

Patient data collection relies heavily on forms and questionnaires, yet their design often lacks sufficient attention and standardization.
Existing medical forms may not be semantically interoperable, hindering data aggregation and analysis.
The RES-Q+ project addresses the need for improved semantic standardization in medical data collection.

Purpose of the Study:

To semantically standardize a widely used international stroke registry data collection form.
To derive general principles for improving the design and semantic alignment of medical forms.
To establish a canonical, knowledge-graph-based representation for medical data collection.

Main Methods:

Analysis of patterns within the RES-Q data collection form.
Application of interoperability standards, including SNOMED CT and HL7 FHIR.
Modeling of residual data categories (e.g., "none", "other", "unknown") using SNOMED CT codes linked to FHIR resources.

Main Results:

Identification of general principles for enhancing medical form design and semantic alignment.
Demonstration of how to model residual data using SNOMED CT and FHIR.
Progress towards a knowledge-graph-based representation for standardized stroke registry data.

Conclusions:

Semantic standardization of medical forms using SNOMED CT and HL7 FHIR is feasible and beneficial.
The derived principles can improve the design and interoperability of various medical data collection instruments.
This work contributes to more robust and semantically rich clinical data management systems.