Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/120843
Title: Definition of a SNOMED CT pathology subset and microglossary, based on 1.17 million biological samples from the Catalan Pathology Registry
Author: Sanz, Xavier
Pareja, Laura
Rius, Ariadna
Rodenas, Pepi
Abdón, Núria
Gálvez, Jordi
Esteban, Laura
Escribà Jordana, Josep M.
Borràs Andrés, Josep Maria
Ribes Puig, Josepa
Keywords: Patologia
Glossaris
Terminologia
Catalunya
Espècimens biològics
Pathology
Glossaries
Terminology
Catalonia
Biological specimens
Issue Date: Feb-2018
Publisher: Elsevier
Abstract: SNOMED CT terminology is not backed by standard norms of encoding among pathologists. The vast number of concepts ordered in hierarchies and axes, together with the lack of rules of use, complicates the functionality of SNOMED CT for coding, extracting, and analyzing the data. Defining subgroups of SNOMED CT by discipline could increase its functionality. The challenge lies in how to choose the concepts to be included in a subset from a total of over 300,000. Besides, SNOMED CT does not cover daily need, as the clinical reality is dynamic and changing. To adapt SNOMED CT to needs in a flexible way, the possibility exists to create extensions. In Catalonia, most pathology departments have been migrating from SNOMED II to SNOMED CT in a bid to advance the development of the Catalan Pathology Registry, which was created in 2014 as a repository for all the pathological diagnoses. This article explains the methodology used to: (a) identify the clinico-pathological entities and the molecular diagnostic procedures not included in SNOMED CT; (b) define the theoretical subset and microglossary of pathology; (c) describe the SNOMED CT concepts used by pathologists of 1.17 million samples of the Catalan Pathology Registry; and d) adapt the theoretical subset and the microglossary according to the actual use of SNOMED CT. Of the 328,365 concepts available for coding the diagnoses (326,732 in SNOMED CT and 1,576 in Catalan extension), only 2% have been used. Combining two axes of SNOMED CT, body structure and clinical findings, has enabled coding most of the morphologies.
Note: Versió postprint del document publicat a: https://doi.org/10.1016/j.jbi.2017.11.010
It is part of: Journal of Biomedical Informatics, 2018, vol. 78, p. 167-176
URI: http://hdl.handle.net/2445/120843
Related resource: https://doi.org/10.1016/j.jbi.2017.11.010
ISSN: 1532-0464
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
Articles publicats en revistes (Ciències Clíniques)

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