Mapping Perinatal Nursing Process Measurement Concepts to Standard Terminologies

Objective: To describe the use of the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) as a model for interoperability of the nursing terminology in the national and international contexts. Methods: This is an integrative literature review according to Cooper, which searched for articles in Portuguese, English and Spanish, published between September 2011 and November 2018 in the BVS, PubMed, SCOPUS, CINAHL, EMBASE, and Web of Science databases, ending in a sample of 15 articles. Results: The SNOMED-CT is a multi-professional nomenclature used by nursing in different care contexts, being associated with other standardized languages of the discipline, such as ICNP®, NANDA-I, and the Omaha System. Conclusion: This review has shown that the use of SNOMED- CT is incipient in the national context, justifying the need to develop studies aimed at mapping the interoperability of existing systems of standardized language, especially NANDA-I, ICNP and Omaha System, in order to adapt the implementation of SNOMED-CT.

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“…Mapping perinatal nursing process measurement concepts to standard terminologies. Ivory (24) USA, 2016 CIN: Computers, Informatics, Nursing…”

Section: Cross-mapping / Levelmentioning

confidence: 99%

SNOMED-CT as a standardized language system model for nursing: an integrative review

Silva

Vega

Córdova

et al. 2020

Rev. Gaúcha Enferm.

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“…The process of cross-terminology mapping evaluates the relationship and equivalence between terms from different terminological systems (1)(2)(3) . The reasons for its use include the promotion of interoperability (4) , data reuse (5) , identification of gaps in terminology (6) and update of terminology versions (7) .…”

Section: Introductionmentioning

confidence: 99%

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing

Torres

Gomes

Ronnau

et al. 2020

Rev. esc. enferm. USP

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This theoretical and reflective study aimed to assess the contribution of the ISO/TR 12300:2016 document for the mapping of nursing terminology. The referred document and related articles were used as an empirical framework. The study analyzed the content of the document, highlighting cardinality and equivalence principles. The standard presents conceptual and operational basis for mapping, with cardinality and equivalence as the support for the categorization of cross-terminology mapping in the area of nursing. Cardinality verifies candidate target terms to represent the source term, while the equivalence degree scale checks semantic correspondence. Among the principles included in the ISO/TR 12300:2016, cardinality and equivalence contribute to the accurate representation of the results of the cross-terminology mapping process and its use should decrease inconsistencies.

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“…The ICNP is an object for mapping that is composed of primitive terms (in a 7-axis model) and precoordinated terms (diagnoses, results, and nursing interventions) represented in the Web Ontology Language [ 10 - 14 ]. It should be noted that by recognizing the use of terminologies for the documentation of the practices of the nursing profession [ 15 ], nursing terminologies can be mapped to each other [ 2 , 10 ] and among other terminologies that are not restricted to the profession [ 13 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Among the challenges of cross-mapping is the high number of terms [ 2 , 11 , 14 ], which implies that cross-mapping is a strenuous, extensive, and tedious process [ 11 , 17 ] that requires time and effort to develop [ 18 ]. Despite initiatives for automation [ 19 , 20 ], called “self-combining mapping” [ 1 ], this process is mostly performed manually [ 2 , 14 , 21 ], which is called “human mapping” [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Results of Manual and Automated Processes of Cross-Mapping Between Nursing Terms: Quantitative Study

et al. 2020

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Background Cross-mapping establishes equivalence between terms from different terminology systems, which is useful for interoperability, updated terminological versions, and reuse of terms. Due to the number of terms to be mapped, this work can be extensive, tedious, and thorough, and it is susceptible to errors; this can be minimized by automated processes, which use computational tools. Objective The aim of this study was to compare the results of manual and automated term mapping processes. Methods In this descriptive, quantitative study, we used the results of two mapping processes as an empirical basis: manual, which used 2638 terms of nurses’ records from a university hospital in southern Brazil and the International Classification for Nursing Practice (ICNP); and automated, which used the same university hospital terms and the primitive terms of the ICNP through MappICNP, an algorithm based on rules of natural language processing. The two processes were compared via equality and exclusivity assessments of new terms of the automated process and of candidate terms. Results The automated process mapped 569/2638 (21.56%) of the source bank’s terms as identical, and the manual process mapped 650/2638 (24.63%) as identical. Regarding new terms, the automated process mapped 1031/2638 (39.08%) of the source bank’s terms as new, while the manual process mapped 1251 (47.42%). In particular, manual mapping identified 101/2638 (3.82%) terms as identical and 429 (16.26%) as new, whereas the automated process identified 20 (0.75%) terms as identical and 209 (7.92%) as new. Of the 209 terms mapped as new by the automated process, it was possible to establish an equivalence with ICNP terms in 48 (23.0%) cases. An analysis of the candidate terms offered by the automated process to the 429 new terms mapped exclusively by the manual process resulted in 100 (23.3%) candidates that had a semantic relationship with the source term. Conclusions The automated and manual processes map identical and new terms in similar ways and can be considered complementary. Direct identification of identical terms and the offering of candidate terms through the automated process facilitate and enhance the results of the mapping; confirmation of the precision of the automated mapping requires further analysis by researchers.

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Mapping Perinatal Nursing Process Measurement Concepts to Standard Terminologies

Cited by 12 publications

References 26 publications

SNOMED-CT as a standardized language system model for nursing: an integrative review

SNOMED-CT as a standardized language system model for nursing: an integrative review

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing

Comparison of the Results of Manual and Automated Processes of Cross-Mapping Between Nursing Terms: Quantitative Study

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