Literature DB >> 22925793

SNOMED CT in pathology.

Marcial García-Rojo1, Christel Daniel, Arvydas Laurinavicius.   

Abstract

Pathology information systems have been using SNOMED II for many years, and in most cases, they are in a migration process to SNOMED CT. COST Action IC0604 (EURO-TELEPATH) has considered terminology normalization one of its strategic objectives. This paper reviews the use of SNOMED CT in healthcare, with a special focus in pathology. Nowadays, SNOMED CT is mainly used for concept search and coding of clinical data. Some ontological errors found in SNOMED CT are described. The Integrating the Healthcare Enterprise (IHE) initiative has fostered the use of SNOMED CT, also in Pathology, as recommended in the Supplement Anatomic Pathology Structured Reports of the IHE Anatomic Pathology Technical Framework. Rule governing concept post-coordination is also described. Some recent initiatives are trying to define a SNOMED CT subset for Pathology. The Spanish Society of Pathology has defined a subset for specimens and procedures in Pathology. Regarding diagnosis coding, the morphological abnormality sub-hierarchy of SNOMED CT need to be significantly extended and improved to become useful for pathologists. A consensus is needed to encode pathology reports with the adequate hierarchies and concepts. This will make the implementation of pathology structured reports more feasible.

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Year:  2012        PMID: 22925793

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  3 in total

1.  Implementing an online reporting system in the anatomical pathology department of a tertiary care teaching hospital in India: a case study.

Authors:  Kedar Radhakrishna; Marjorie Correa; Deepak Thounaojam; Tony D S Raj
Journal:  Perspect Health Inf Manag       Date:  2013-07-01

2.  Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue.

Authors:  Karin Lindman; Jerómino F Rose; Martin Lindvall; Claes Lundström; Darren Treanor
Journal:  J Pathol Inform       Date:  2019-07-23

3.  Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.

Authors:  Yoojoong Kim; Jeong Hyeon Lee; Sunho Choi; Jeong Moon Lee; Jong-Ho Kim; Junhee Seok; Hyung Joon Joo
Journal:  Sci Rep       Date:  2020-11-20       Impact factor: 4.379

  3 in total

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