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Semantics in action: a guide for representing clinical data elements with SNOMED CT.

Julien Ehrsam1,2, Christophe Gaudet-Blavignac3,4, Mirjam Mattei3,5

  • 1Division of Medical Information Sciences, Diagnostic Department, University Hospitals of Geneva, Rue Gabrielle-Perret-Gentil 4, Geneva, 1205, Switzerland. Julien.Ehrsam@hug.ch.

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Summary

This study introduces a practical guide for semantic representation of clinical data using SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms). The eight-rule framework enhances data accuracy, consistency, and reusability for improved healthcare research and public health initiatives.

Keywords:
Data warehouseKnowledge representationManual semantic annotationSNOMED CTSemantic interoperability

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Area of Science:

  • Clinical Informatics
  • Health Data Management
  • Medical Terminology

Background:

  • Abundant clinical data lacks meaningful reuse due to inconsistent semantic representation.
  • SNOMED CT offers potential for improving research, public health, and care quality.
  • Lack of industrialised guidelines hinders sustainable and reproducible semantic data representation.

Purpose of the Study:

  • To describe a practical guide for semantic representation of data elements using SNOMED CT.
  • To address challenges encountered during the large-scale application of SNOMED CT.
  • To develop additional rules beyond existing guidelines for industrial application.

Main Methods:

  • An iterative, eight-rule step-by-step guide was developed through focus groups.
  • The guide was refined based on practical usage and growing coverage.
  • Rules were tested to ensure they achieve desired outcomes, prioritizing semantic accuracy.

Main Results:

  • A practical framework with eight rules for SNOMED CT semantic representation was developed.
  • The rules are categorized into understanding data context and proper SNOMED CT usage (single concepts, approved/extended post-coordination).
  • The guide addresses large-scale implementation challenges, promoting accuracy and consistency.

Conclusions:

  • The developed guide provides a practical framework for accurate and consistent semantic representation using SNOMED CT.
  • It supports a shift towards semantic-centric models, enhancing clinical data interoperability and reuse.
  • The framework facilitates a common method for large-scale SNOMED CT implementation in healthcare.