Standardizing Physiologic Assessment Data to Enable Big Data Analytics

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.