Standardizing Physiologic Assessment Data to Enable Big Data Analytics. (January 2017)

• Record Type: Journal Article
• Authors: Matney, Susan A.; Settergren, Theresa (Tess); Carrington, Jane M.; Richesson, Rachel L.; Sheide, Amy; Westra, Bonnie L.
• Is Part Of: Western journal of nursing research. Volume 39:Number 1(2017)
• Page Start: 63
• Page End: 77
• Publication Date: 2017-01
• Subjects: data exchange standards -- nursing assessment -- nursing informatics -- multi-institutional research -- LOINC -- SNOMED CT -- medical surgical nursing; Nursing -- Research -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- West (U.S.) -- Periodicals; Nursing -- West (U.S.) -- Periodicals; Nursing -- Periodicals; Nursing Research -- Periodicals; Soins infirmiers -- Recherche -- Périodiques; Soins infirmiers -- Périodiques; Soins infirmiers -- Recherche -- États-Unis (Ouest) -- Périodiques; Soins infirmiers -- États-Unis (Ouest) -- Périodiques; Soins infirmiers; Recherche; États-Unis (Ouest); Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 610.730702
• DOI: 10.1177/0193945916659471
• Languages: English
• ISSNs: 0193-9459

Abstract

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.