Taxonomic Triangulation of Care in Healthcare Protocols: Mapping of Diagnostic Knowledge From Standardized Language.

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.

Predictive Tools in the Care of Blood Donors: Prevention of Vasovagal Syndrome.

Prediction in healthcare is essential in order to promote safe and quality care. Taking adequate care of blood donors, who perform an altruistic act towards society, is paramount. Therefore, the use of tools which allow to predict the risk of Vasovagal Syndrome during the act of blood donation is necessary. The objective of this study is to design a predictive engine of an expert system to determine the risk of Vasovagal Syndrome through the use of deductive methodology. Five clusters of predictors of this syndrome were obtained by applying grouping tables of the variables established by logical formulation in such a way that after combinatorial variables, 5 values were obtained for the determination of risk using a Lickert scale. With these results we could design the predictive engine that will allow the development of a computational tool to improve the quality of care of blood donors.