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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao AORN Journalarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
AORN Journal
Article . 2016 . Peer-reviewed
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AORN Journal
Article . 2017
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Big Data and Perioperative Nursing

Authors: Jessica J. Peterson; Bonnie L. Westra;

Big Data and Perioperative Nursing

Abstract

AbstractBig data are large volumes of digital data that can be collected from disparate sources and are challenging to analyze. These data are often described with the five “Vs”: volume, velocity, variety, veracity, and value. Perioperative nurses contribute to big data through documentation in the electronic health record during routine surgical care, and these data have implications for clinical decision making, administrative decisions, quality improvement, and big data science. This article explores methods to improve the quality of perioperative nursing data and provides examples of how these data can be combined with broader nursing data for quality improvement. We also discuss a national action plan for nursing knowledge and big data science and how perioperative nurses can engage in collaborative actions to transform health care. Standardized perioperative nursing data has the potential to affect care far beyond the original patient.

Keywords

Pressure Ulcer, Venous Thrombosis, Data Collection, Clinical Decision-Making, Patient Handoff, Documentation, Quality Improvement, Catheter-Related Infections, Perioperative Nursing, Urinary Tract Infections, Electronic Health Records, Humans, Delivery of Health Care

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    13
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
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