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Big data-enabled nursing : education, research and practice / edited by Connie W. Delaney, Charlotte A. Weaver, Judith J. Warren, Thomas R. Clancy, Roy L. Simpson.

9783319533001 (electronic book)
3319533002 (electronic book)
9783319532998
3319532995

Cham : Springer, 2017.

English

1 online resource (xxxv, 488 pages) : illustrations.

10.1007/978-3-319-53300-1 doi

RT50.5

610.730285

This text reflects how the learning health system infrastructure is maturing and being advanced by health information exchanges (HIEs) with multiple organizations blending their data or enabling distributed computing.  It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery.  Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing has consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Big Data-Enabled Nursing reflects on how health systems have developed and how electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. It provides instruction on the new opportunities for nursing and educates readers on the new skills in research methodologies that are being further enabled by new partnerships spanning all sectors. .

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed November 13, 2017).

Health informatics, 1431-1917

9783319532998

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