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Review
. 2015:2015:639021.
doi: 10.1155/2015/639021. Epub 2015 Jun 2.

Toward a Literature-Driven Definition of Big Data in Healthcare

Emilie Baro  1 , Samuel Degoul  1 , Régis Beuscart  1 , Emmanuel Chazard  1
Affiliations
Review

Toward a Literature-Driven Definition of Big Data in Healthcare

Emilie Baro et al. Biomed Res Int. 2015.

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 (n) and the number of variables (p) 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 Log(n∗p) ≥ 7. 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.

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Figures

Figure 1
Figure 1
Flowchart of the literature review.
Figure 2
Figure 2
Number of papers about big data in healthcare published per year (full years only).
Figure 3
Figure 3
Number of papers about big data in healthcare describing a dataset per year (full years only).
Figure 4
Figure 4
Log(n∗p) per year of publication. The continuous line represents the linear regression (P = 0.34).
Figure 5
Figure 5
Representation of the probability density function of Log(n) for omics, medical specialties, public health, and all fields together.
Figure 6
Figure 6
Representation of the probability density function of Log(p) for omics, medical specialties, public health, and all fields together.
Figure 7
Figure 7
Representation of the probability density function of Log(n∗p) for omics, medical specialties, public health, and all fields together.
Figure 8
Figure 8
Log(p) as a function of Log(n) for omics, medical specialties, and public health. Each pictogram stands for one paper.
See this image and copyright information in PMC

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