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BioMed Research International
Volume 2015, Article ID 639021, 9 pages
http://dx.doi.org/10.1155/2015/639021
Review Article

Toward a Literature-Driven Definition of Big Data in Healthcare

Department of Public Health, EA 2694, University of Lille, 1 Place de Verdun, 59045 Lille Cedex, France

Received 13 November 2014; Accepted 4 February 2015

Academic Editor: Shahram Shirani

Copyright © 2015 Emilie Baro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals and the number of variables for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with . Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.