ArticleJMIR nursing
Comparison of the Results of Manual and Automated Processes of Cross-Mapping Between Nursing Terms: Quantitative Study.
Article in JMIR nursing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Automated Identification of Nursing Diagnoses and Interventions From Nursing Records Using a Retrieval-Augmented Large Language Model Approach: Quantitative Study.Journal of medical Internet research · 2026Article
- Clinical Terminology Mapping Service Based on Information Retrieval.Methods of information in medicine · 2025Article
- Discrepancies in Aggregate Patient Data between Two Sources with Data Originating from the Same Electronic Health Record: A Case Study.Applied clinical informatics · 2025Article
- Standardizing nursing data extracted from electronic health records for integration into a statewide clinical data research network.International journal of medical informatics · 2024Article
- Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCross-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.
objectiveThe aim of this study was to compare the results of manual and automated term mapping processes.
methodsIn 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.
resultsThe 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.
conclusionsThe 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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