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Implications for Nursing Research and Generation of Evidence

Implications for Nursing Research and Generation of Evidence
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Author(s): Suzanne Bakken (Columbia University, USA), Robert Lucero (Columbia University, USA), Sunmoo Yoon (Columbia University, USA) and Nicholas Hardiker (University of Salford, UK)
Copyright: 2013
Pages: 15
Source title: Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch055

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Abstract

A sound informatics infrastructure is essential to optimise the application of evidence in nursing practice. A comprehensive review of the infrastructure and associated research methods is supported by an extensive resource of references to point the interested reader to further resources for more in depth study. Information and communication technology (ICT) has been recognized as a fundamental component of applying evidence to practice for several decades. Although the role of ICT in generating knowledge from practice was formally identified as a nursing informatics research priority in the early 1990s (NINR Priority Expert Panel on Nursing Informatics, 1993), it has received heightened interest recently. In this chapter, the authors summarize some important trends in research that motivate increased attention to practice-based generation of evidence. These include an increased emphasis on interdisciplinary, translational, and comparative effectiveness research; novel research designs; frameworks and models that inform generation of evidence from practice; and creation of data sets that include not only variables related to biological and genetic measures, but also social and behavioral variables. The chapter also includes an overview of the ICT infrastructure and informatics processes required to facilitate generation of evidence from practice and across research studies: (1) information structures (e.g., re-usable concept representations, tailored templates for data acquisition), (2) processes (e.g., data mining algorithms, natural language processing), and (3) technologies (e.g., data repositories, visualization tools that optimize cognitive support). In addition, the authors identify key knowledge gaps related to informatics support for nursing research and generation of evidence from practice.

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