Standardizing Physiologic Assessment Data to Enable Big Data Analytics

Susan A. Matney, Theresa (Tess) Settergren, Jane M. Carrington, Rachel L. Richesson, Amy Sheide, Bonnie L. Westra

Research output: Contribution to journalArticle

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)63-77
Number of pages15
JournalWestern journal of nursing research
Volume39
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • LOINC
  • SNOMED CT
  • data exchange standards
  • medical surgical nursing
  • multi-institutional research
  • nursing assessment
  • nursing informatics

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    Matney, S. A., Settergren, T. T., Carrington, J. M., Richesson, R. L., Sheide, A., & Westra, B. L. (2017). Standardizing Physiologic Assessment Data to Enable Big Data Analytics. Western journal of nursing research, 39(1), 63-77. https://doi.org/10.1177/0193945916659471