Literature DB >> 17625400

Nursing interventions classification in systematized nomenclature of medicine clinical terms: a cross-mapping validation.

Hyun-Tae Park1, Der-Fa Lu, Debra Konicek, Connie Delaney.   

Abstract

The Systemized Nomenclature of Medical Clinical Terms, or SNOMED-CT, was developed to create a comprehensive clinical healthcare reference terminology. Standardized nursing language concepts and terminologies recognized by the American Nurses Association have been added to SNOMED-CT and include the NANDA's Taxonomy II, NIC, NOC, the Omaha System, and CCC. The relationship link between terminologies and SNOMED-CT is provided in a mapping table, which identifies the source terminology. The purpose of this study is to examine the validity of the cross-mapping between the source system (NIC) and the target system (SNOMED-CT) using the methodology developed by Lu and colleagues to detect misassigned concepts. Knowledge representation concepts in the NIC and SNOMED-CT systems were compared using expert human judgment. Of 514 NIC concepts, 14 (2.7%) were identified as misassigned in SNOMED-CT. Two inappropriate representations of concepts were discovered in NIC. Results and recommendations were given to NIC and to SNOMED-CT.

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Year:  2007        PMID: 17625400     DOI: 10.1097/01.NCN.0000280590.35690.7d

Source DB:  PubMed          Journal:  Comput Inform Nurs        ISSN: 1538-2931            Impact factor:   1.985


  3 in total

1.  Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings.

Authors:  Guan N Guo; Jitendra Jonnagaddala; Sanjay Farshid; Vojtech Huser; Christian Reich; Siaw-Teng Liaw
Journal:  J Am Med Inform Assoc       Date:  2019-11-01       Impact factor: 4.497

2.  Using the nursing interventions classification as a potential measure of nurse workload.

Authors:  Pamela B de Cordova; Robert J Lucero; Sookyung Hyun; Patricia Quinlan; Kwanza Price; Patricia W Stone
Journal:  J Nurs Care Qual       Date:  2010 Jan-Mar       Impact factor: 1.597

3.  Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?

Authors:  Andrew D Boyd; Karen Dunn Lopez; Camillo Lugaresi; Tamara Macieira; Vanessa Sousa; Sabita Acharya; Abhinaya Balasubramanian; Khawllah Roussi; Gail M Keenan; Yves A Lussier; Jianrong 'John' Li; Michel Burton; Barbara Di Eugenio
Journal:  Int J Med Inform       Date:  2018-02-09       Impact factor: 4.046

  3 in total

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