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Big data, Nursing science, Systematic review



  1. Caruso, Rosario PhD, RN
  2. Arrigoni, Cristina MSN, RN
  3. Conte, Gianluca RN
  4. Rocco, Gennaro PhD, RN, FAAN
  5. Dellafiore, Federica PhD, RN
  6. Ambrogi, Federico PhD
  7. Stievano, Alessandro PhD, RN, FAAN


Big data have the potential to determine enhanced decision-making process and to personalize the approach of delivering care when applied in nursing science. So far, the literature on this topic is still not synthesized for the period between 2014 and 2018. Thus, this systematic review aimed to identify and synthesize the most recent evidence on big data application in nursing research. The systematic search was undertaken for the evidence published from January 2014 to May 2018, and the outputs were formatted using the PRISMA Flow Diagram, whereas the quality appraisal was addressed by recommendations consistent with the Critical Appraisal Skills Program. Twelve studies on big data in nursing were included and divided into two themes: the majority of the studies aimed to determine prediction assessment, while only four studies were related to the impact of big data applications to support clinical practice. This review tracks the recent state of knowledge on big data applications in nursing science, revealing the potential for nursing engagement in big data science, even if currently limited to some fields. Big data applications in nursing might have a tremendous potential impact, but are currently underused in research and clinical practice.