TY - CHAP

T1 - Data Science in Healthcare

T2 - Benefits, Challenges and Opportunities

AU - Abedjan, Ziawasch

AU - Boujemaa, Nozha

AU - Campbell, Stuart

AU - Casla, Patricia

AU - Lojo, Aizea

AU - Chatterjea, Supriyo

AU - Consoli, Sergio

AU - Despenic, Marija

AU - Heinrich, Adrienne

AU - Kustra, Jacek

AU - Petković, Milan

AU - Costa-Soria, Cristobal

AU - Czech, Paul

AU - Garattini, Chiara

AU - Hamelinck, Dirk

AU - Verachtert, WIlfried

AU - Wuyts, Roel

AU - Kraaij, Wessel

AU - Sanchez, Marga Martin

AU - Mayer, Miguel A.

AU - Melideo, Matteo

AU - Menasalvas Ruiz, Ernestina

AU - Rodriguez Gonzalez, Alejandro

AU - Aarestrup, Frank Moller

AU - Narro Artigot, Elvira

AU - Reforgiato Recupero, Diego

AU - Roesems Kerremans, Gisele

AU - Ruping, Stefan

AU - Sasaki, Felix

AU - Spek, Wouter

AU - Stojanovic, Nenad

AU - Vasiljevs, Andrejs

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019/2/24

Y1 - 2019/2/24

N2 - The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain.

AB - The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain.

UR - http://www.scopus.com/inward/record.url?scp=85064376260&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-05249-2_1

DO - 10.1007/978-3-030-05249-2_1

M3 - Contribution to book/anthology

SN - 978-3-030-05248-5

SP - 3

EP - 38

BT - Data Science for Healthcare

A2 - Consoli, Sergio

A2 - Reforgiato Recupero, Diego

A2 - Petković, Milan

ER -