Since a substantial component of health care delivery is reflected in nursing's work, it is imperative that nursing expedites implementation of a standardized language that reflects nursing's work and ultimately allows outcome evaluation. This paper will summarize the state of development and related issues of standardized language in nursing, including: Nursing Minimum Data Set, Taxonomies of Nursing Diagnoses, Nursing Interventions, Outcomes, and the Nursing Management Minimum Data Set. The Nursing Minimum Data Set, including nursing care, patient or client demographic, and service elements, reflects a standardized collection of essential nursing data used by multiple data users in the health care delivery system across all types of settings. The nursing care elements include nursing diagnosis, nursing intervention, nursing outcome, and intensity of nursing care. Currently, more than 100 nursing diagnoses have been accepted for clinical testing by the North American Nursing Diagnosis Association (NANDA) and have been incorporated into a taxonomy of nursing diagnoses that reflects patient responses to actual or potential health problems that nursing can address. A current formulation of a taxonomy of nursing interventions for the treatment of the nursing diagnoses yielded 336 nursing intervention labels organized at three or four levels of abstraction. Concomitant with these endeavors is the necessity for identifying outcomes associated with each diagnosis and its treatment. Concepts and a classification for indicators of these outcomes are being reviewed. Last, to address the contextual covariates of patient outcomes, a collection of core variables needed by nurse managers to make management decisions and compare nursing effectiveness across institutions and geographic regions is under development. In summary, standardized measures to determine cost effective, high quality, appropriate outcomes of nursing care delivered across settings and sites are being developed.
Many standardized healthcare languages have been mapped to the Systematized Nomenclature of Medicine Clinical Terms known as SNOMED CT, which was developed by the College of American Pathologists. This study describes a methodology for detecting misassigned concepts from source systems to SNOMED CT and presents the results of applying this methodology to a subset of concepts from two standardized nursing languages, the Nursing Interventions Classification and the Nursing Outcomes Classification. The methodology is based on comparing the knowledge representations of a set of nursing concepts between source systems (nursing languages) and SNOMED CT. If any nursing concept differs in knowledge representation in the target system compared with the source system, editorial misassignment of the concept was declared and recommendations for target system developers were made. In a total of 75 nursing concepts used to test this method, five misassigned concepts(6.6%) were found in SNOMED CT. This method can be used to validate other healthcare languages.
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