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Network Nursing: Connections Between Nursing and Complex Network Science

Authors

  • Matthias Dehmer PhD,

    Professor and Head of Institute, Corresponding author
    • Department for Biomedical Informatics and Mechatronics, Institute for Bioinformatics and Translational Research, UMIT, Hall, Tyrol, Austria
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  • Werner O. Hackl DI, BSc,

    University Assistant
    1. Department for Biomedical Informatics and Mechatronics, Institute of Health Informatics, UMIT, Hall, Tyrol, Austria
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  • Frank Emmert-Streib PhD,

    Senior Lecturer
    1. Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
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  • Eva Schulc PhD, PTA,

    University Assistant
    1. Department of Nursing Science and Gerontology, Institute for Nursing Science, UMIT, Hall, Tyrol, Austria
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  • Christa Them PhD, RN

    Professor and Head of Institute
    1. Department of Nursing Science and Gerontology, Institute for Nursing Science, UMIT, Hall, Tyrol, Austria
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  • Author Note

    Matthias Dehmer thanks the Standortagentur Tirol for supporting this work. Werner O. Hackl has received funding from Tiroler Wissenschaftsfonds (UNI-0404/1189).

Author contact: matthias.dehmer@umit.at, with a copy to the Editor: journal@nanda.org

Abstract

Purpose

Nursing processes are complex and include quite a few nonlinear interrelationships that are difficult to discover. Moreover, the formal description, visualization, and analysis of these processes are nontrivial challenges. The purpose of this paper is to establish the term network nursing as synonym for using quantitative graph theory in nursing science and to discuss how network nursing can be used for tackling such complex challenges in nursing science.

Methods

In particular, methods from quantitative graph theory, divided into two major classes, comparative network analysis and network characterization, are employed to solve challenging problems in nursing.

Findings

We demonstrate by way of example that this mathematical apparatus is feasible to tackle research questions when modeling and analyzing nursing processes.

Here we use a “NANDA-I” showcase to illustrate how nursing networks can be derived from nursing data and how these networks can be used to compare different patients regarding their nursing diagnoses.

Conclusions

Nursing networks can be used to characterize patients from a nursing perspective. Especially, they allow a process-based view and are able to map relationships or dependencies. One major advantage of a networking approach is that it can be applied independently from the underlying nursing classifications or terminologies.

Implications for Nursing Practice

Network nursing makes it possible to formally investigate the nursing process and thus opens up a so far little-known cosmos of possibilities and methods to expand nursing knowledge.

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