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'SNOMEDizing' Questionnaires for Standardizing Stroke Registry Data.

Andrea Riedel1, Stefan Schulz2, Catalina Martínez Costa3

  • 1Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Studies in Health Technology and Informatics
|August 23, 2024
PubMed
Summary
This summary is machine-generated.

Standardizing healthcare data is crucial for international interoperability. Evaluating SNOMED CT for the European Stroke Registry showed it can represent most questionnaire data, but requires guidelines and potential extensions for full integration.

Keywords:
Information modelInteroperabilitySNOMED CTStroke

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Area of Science:

  • Health Informatics
  • Medical Terminology
  • Data Standards

Background:

  • International interoperability of healthcare and research data necessitates adherence to established standards.
  • The European Registry of Stroke Care Quality utilizes questionnaires, requiring a robust method for data representation.

Purpose of the Study:

  • To evaluate the suitability of SNOMED CT for representing questionnaire items from the European Registry of Stroke Care Quality.
  • To assess the extent of information representable by SNOMED CT and identify areas needing further development.

Main Methods:

  • A complex annotation protocol was employed to map questionnaire items to SNOMED CT concepts.
  • Inter-annotator agreement was calculated to assess the reliability of the annotation process.

Main Results:

  • The agreement between validators and annotators was 72.4%.
  • SNOMED CT, including complex post-coordinations, could represent at least 64% of the information.
  • 9% of the information required an information model, and 14% necessitated new SNOMED CT content.

Conclusions:

  • SNOMED CT demonstrates significant potential for representing stroke registry questionnaire data.
  • Further development, including annotation guidelines, specific reference sets, and integration with information models like HL7 FHIR, is recommended for comprehensive data interoperability.