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Semantic Mapping of German Nursing Diagnoses in SNOMED CT: Risks and Challenges
Jessica Ferreira da Silva Marques1, Elizaveta Beeck1, Antonia Schnelnast2
1UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.
Background:
Nursing diagnoses and interventions are essential components of clinical documentation and patient-centered care, yet nursing data in German-speaking healthcare settings are commonly documented using local terminologies.
Objectives:
This paper aims to analyze translation- and modeling-related challenges when mapping German nursing diagnoses to SNOMED CT.
Methods:
Nursing diagnoses from the DiZiMa® catalog were translated and mapped to SNOMED CT using a structured semantic mapping approach. Two large language models (ChatGPT and Microsoft Copilot) were used in parallel to support translation, guided by established principles of scientific translation.
Results:
While 98.6% of the diagnoses could be mapped to SNOMED CT, 27.2% showed semantic precision loss, particularly for risk diagnoses and context-dependent nursing concepts, often requiring postcoordination, or remaining unmapped (1.4%).
Conclusion:
Semantic interoperability of nursing data requires more than direct translation or simple 1:1 mapping, highlighting the need for nursing-specific modeling strategies.
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