Nursing Knowledge. Issue 4 (October 2015)

• Record Type: Journal Article
• Authors: Westra, Bonnie L.; Clancy, Thomas R.; Sensmeier, Joyce; Warren, Judith J.; Weaver, Charlotte; Delaney, Connie W.
• Is Part Of: Nursing administration quarterly. Volume 39:Issue 4(2015)
• Publication Date: 2015-10
• Subjects: Nursing services -- Administration -- Periodicals; 610.73068
• DOI: 10.1097/NAQ.0000000000000130
• Languages: English
• ISSNs: 0363-9568

Abstract

<abstract> <title> <x xml:space="preserve">Abstract</x> </title> <sec> <p>The integration of Big Data from electronic health records and other information systems within and across health care enterprises provides an opportunity to develop actionable predictive models that can increase the confidence of nursing leaders' decisions to improve patient outcomes and safety and control costs. As health care shifts to the community, mobile health applications add to the Big Data available. There is an evolving national action plan that includes nursing data in Big Data science, spearheaded by the University of Minnesota School of Nursing. For the past 3 years, diverse stakeholders from practice, industry, education, research, and professional organizations have collaborated through the "Nursing Knowledge: Big Data Science" conferences to create and act on recommendations for inclusion of nursing data, integrated with patient-generated, interprofessional, and contextual data. It is critical for nursing leaders to understand the value of Big Data science and the ways to standardize data and workflow processes to take advantage of newer cutting edge analytics to support analytic methods to control costs and improve patient quality and safety.</p> </sec> </abstract>