Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge.
The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals.
A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice.
Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods.
There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.
Authors
Bonnie L. Westra, PhD, RN, FAAN, FACMI
Martha Sylvia, PhD, MBA, RN
Elizabeth F. Weinfurter, MLIS
Lisiane Pruinelli, PhD, RN
Jung In Park, PhD, RN
Dianna Dodd, MSN, RN, RN-BC, CCM
Gail M. Keenan, PhD, RN, FAAN
Patricia Senk, PhD, RN
Rachel L. Richesson, PhD, MS, MPH, FACMI
Vicki Baukner, MS, RN
Christopher Cruz, BS, RN
Grace Gao, DNP, RN
Luann Whittenburg, PhD, RN BC, FHIMSS, FAAN
Connie W. Delaney, PhD, RN, FAAN, FACMI