Linked Life Data

11604723

Source:http://linkedlifedata.com/resource/pubmed/id/11604723

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2001-10-17
pubmed:abstractText
We evaluated the utility of the CEN Categorical Structure for Nursing Diagnoses as a terminology model for integrating nursing diagnosis concepts into SNOMED. First, we dissected nursing diagnosis term phrases from two source terminologies (North American Nursing Diagnosis Association (NANDA) Taxonomy 1 and Omaha System) into the semantic categories of the CEN categorical structure. Second, we critically analyzed the similarities between the semantic links in the CEN model and the semantic links used to formally define diagnostic concepts in SNOMED RT and SNOMED CT. Our findings demonstrated that focus, bearer, and judgment were present in 100% of the NANDA and Omaha term phrases. The Omaha term phrases contained no additional descriptors beyond those considered mandatory in the CEN model. In contrast, at least 3% of NANDA diagnoses included a term in each semantic category of the categorical structure. The comparison among the semantic links showed that neither SNOMED RT and SNOMED CT currently contain all the semantic links needed to model the two source terminologies for integration. In conclusion, our findings support the potential utility of the CEN categorical structure as a terminology model for dissecting nursing diagnostic concepts for integration into SNOMED RT and SNOMED CT. However, in order to accomplish this task, appropriate semantic links must be added to SNOMED RT and SNOMED CT.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
84
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
151-5
pubmed:dateRevised
2008-7-10
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
An evaluation of the utility of the CEN categorical structure for nursing diagnoses as a terminology model for integrating nursing diagnosis concepts into SNOMED.
pubmed:affiliation
School of Nursing and Department of Medical Informatics, Columbia University, New York, New York 10032, USA. suzanne.bakken@dmi.columbia.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Evaluation Studies