Title :

Toward a literature-driven definition of big data in healthcare

Author(s) :

Baro, Emilie [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Degoul, Samuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Beuscart, Regis [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Chazard, Emmanuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]

Journal title :

BioMed research international

Abbreviated title :

Biomed Res. Int.

Publication date :

2015-01-01

ISSN :

2314-6133

English keyword(s) :

Mesh:Delivery of Health Care*
Mesh:PubMed*
Mesh:Information Dissemination
Mesh:Humans
Mesh:Electronic Health Records*
Mesh:Publications

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

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 ...
Show more >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. CONCLUSIONS: 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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CHU Lille
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2019-12-09T16:52:17Z
2020-04-01T09:20:33Z