Author Affiliations: Henares University Hospital (Ms González-Aguña), Meco Health Center (Drs Fernández-Batalla and Santamaría-García), Community of Madrid Health Service; Official College of Nursing of Madrid (Ms Gasco-González); Sanitas University Hospital "La Zarzuela" (Ms Cercas-Duque); Department of Computer Science, University of Alcalá (Dr Jiménez-Rodríguez); and Research Group MISKC, University of Alcalá (Ms González-Aguña, Dr Fernández-Batalla, Dr Santamaría-García, Ms Cercas-Duque and Dr Jiménez-Rodríguez), Madrid, Spain.
Comput Inform Nurs. 2020 Jul 24;39(3):145-153. doi: 10.1097/CIN.0000000000000662.
Taxonomic triangulation is a data mining technique for the management of care knowledge. This technique uses standardized languages, such as North American Nursing Diagnosis Association International, Nursing Outcomes Classification, and Nursing Interventions Classification, as well as logic. Its purpose is to find patterns in the data and identify care diagnoses. Triangulation can be applied to databases (clinical records) or to bibliographic sources (eg, protocols). The objective of this study is to identify the care diagnoses implicit in the nursing care protocols of the Community of Madrid. The method followed has three phases: knowledge extraction for mapping of variables, linking to diagnoses, and triangulation with analysis. The study analyzes six protocols, and 344 variables (167 assessment, 29 planning, and 148 intervention) and 6118 links have been extracted. Triangulation identified 165 NANDA diagnoses (68.48%), and only 25 labels were not revealed through this process. As a limitation, the results depend on the knowledge presented in protocols and change with language editions. Some labels included in the sample are recent and are not included in the links with nursing outcomes classification and nursing interventions classification. In conclusion, taxonomic triangulation makes it possible to manage knowledge, discover data patterns, and represent care situations.
分类三角测量是一种用于护理知识管理的数据挖掘技术。该技术使用标准化语言，如北美护理诊断协会国际、护理结局分类和护理干预分类，以及逻辑。其目的是在数据中找到模式并识别护理诊断。三角测量可以应用于数据库（临床记录）或文献来源（例如，方案）。本研究的目的是确定马德里社区护理方案中隐含的护理诊断。所遵循的方法有三个阶段：知识提取用于变量映射、链接到诊断和与分析的三角测量。该研究分析了六个方案，提取了 344 个变量（167 个评估、29 个计划和 148 个干预）和 6118 个链接。三角测量确定了 165 个 NANDA 诊断（68.48%），只有 25 个标签未通过此过程揭示。作为一个限制，结果取决于方案中呈现的知识，并随语言版本而变化。样本中包含的一些标签是最近的，并且未包含在与护理结局分类和护理干预分类的链接中。总之，分类三角测量使得管理知识、发现数据模式和表示护理情况成为可能。
