Data science in healthcare: Benefits, challenges and opportunities

Ziawasch Abedjan, Nozha Boujemaa, Stuart Campbell, Patricia Casla, Supriyo Chatterjea, Sergio Consoli*, Cristobal Costa-Soria, Paul Czech, Marija Despenic, Chiara Garattini, Dirk Hamelinck, Adrienne Heinrich, Wessel Kraaij, Jacek Kustra, Aizea Lojo, Marga Martin Sanchez, Miguel A. Mayer, Matteo Melideo, Ernestina Menasalvas, Frank Møller AarestrupElvira Narro Artigot, Milan Petković, Diego Reforgiato Recupero, Alejandro Rodriguez Gonzalez, Gisele Roesems Kerremans, Roland Roller, Mario Romao, Stefan Ruping, Felix Sasaki, Wouter Spek, Nenad Stojanovic, Jack Thoms, Andrejs Vasiljevs, Wilfried Verachtert, Roel Wuyts

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationData Science for Healthcare : Methodologies and Applications
PublisherSpringer
Publication date2019
Pages3-38
ISBN (Print)9783030052485
ISBN (Electronic)9783030052492
DOIs
Publication statusPublished - 2019

Cite this

Abedjan, Z., Boujemaa, N., Campbell, S., Casla, P., Chatterjea, S., Consoli, S., ... Wuyts, R. (2019). Data science in healthcare: Benefits, challenges and opportunities. In Data Science for Healthcare: Methodologies and Applications (pp. 3-38). Springer. https://doi.org/10.1007/978-3-030-05249-2_1
Abedjan, Ziawasch ; Boujemaa, Nozha ; Campbell, Stuart ; Casla, Patricia ; Chatterjea, Supriyo ; Consoli, Sergio ; Costa-Soria, Cristobal ; Czech, Paul ; Despenic, Marija ; Garattini, Chiara ; Hamelinck, Dirk ; Heinrich, Adrienne ; Kraaij, Wessel ; Kustra, Jacek ; Lojo, Aizea ; Sanchez, Marga Martin ; Mayer, Miguel A. ; Melideo, Matteo ; Menasalvas, Ernestina ; Møller Aarestrup, Frank ; Artigot, Elvira Narro ; Petković, Milan ; Recupero, Diego Reforgiato ; Gonzalez, Alejandro Rodriguez ; Kerremans, Gisele Roesems ; Roller, Roland ; Romao, Mario ; Ruping, Stefan ; Sasaki, Felix ; Spek, Wouter ; Stojanovic, Nenad ; Thoms, Jack ; Vasiljevs, Andrejs ; Verachtert, Wilfried ; Wuyts, Roel. / Data science in healthcare : Benefits, challenges and opportunities. Data Science for Healthcare: Methodologies and Applications. Springer, 2019. pp. 3-38
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author = "Ziawasch Abedjan and Nozha Boujemaa and Stuart Campbell and Patricia Casla and Supriyo Chatterjea and Sergio Consoli and Cristobal Costa-Soria and Paul Czech and Marija Despenic and Chiara Garattini and Dirk Hamelinck and Adrienne Heinrich and Wessel Kraaij and Jacek Kustra and Aizea Lojo and Sanchez, {Marga Martin} and Mayer, {Miguel A.} and Matteo Melideo and Ernestina Menasalvas and {M{\o}ller Aarestrup}, Frank and Artigot, {Elvira Narro} and Milan Petković and Recupero, {Diego Reforgiato} and Gonzalez, {Alejandro Rodriguez} and Kerremans, {Gisele Roesems} and Roland Roller and Mario Romao and Stefan Ruping and Felix Sasaki and Wouter Spek and Nenad Stojanovic and Jack Thoms and Andrejs Vasiljevs and Wilfried Verachtert and Roel Wuyts",
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Abedjan, Z, Boujemaa, N, Campbell, S, Casla, P, Chatterjea, S, Consoli, S, Costa-Soria, C, Czech, P, Despenic, M, Garattini, C, Hamelinck, D, Heinrich, A, Kraaij, W, Kustra, J, Lojo, A, Sanchez, MM, Mayer, MA, Melideo, M, Menasalvas, E, Møller Aarestrup, F, Artigot, EN, Petković, M, Recupero, DR, Gonzalez, AR, Kerremans, GR, Roller, R, Romao, M, Ruping, S, Sasaki, F, Spek, W, Stojanovic, N, Thoms, J, Vasiljevs, A, Verachtert, W & Wuyts, R 2019, Data science in healthcare: Benefits, challenges and opportunities. in Data Science for Healthcare: Methodologies and Applications. Springer, pp. 3-38. https://doi.org/10.1007/978-3-030-05249-2_1

Data science in healthcare : Benefits, challenges and opportunities. / Abedjan, Ziawasch; Boujemaa, Nozha; Campbell, Stuart; Casla, Patricia; Chatterjea, Supriyo; Consoli, Sergio; Costa-Soria, Cristobal; Czech, Paul; Despenic, Marija; Garattini, Chiara; Hamelinck, Dirk; Heinrich, Adrienne; Kraaij, Wessel; Kustra, Jacek; Lojo, Aizea; Sanchez, Marga Martin; Mayer, Miguel A.; Melideo, Matteo; Menasalvas, Ernestina; Møller Aarestrup, Frank ; Artigot, Elvira Narro; Petković, Milan; Recupero, Diego Reforgiato; Gonzalez, Alejandro Rodriguez; Kerremans, Gisele Roesems; Roller, Roland; Romao, Mario; Ruping, Stefan; Sasaki, Felix; Spek, Wouter; Stojanovic, Nenad; Thoms, Jack; Vasiljevs, Andrejs; Verachtert, Wilfried; Wuyts, Roel.

Data Science for Healthcare: Methodologies and Applications. Springer, 2019. p. 3-38.

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

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 - Chatterjea, Supriyo

AU - Consoli, Sergio

AU - Costa-Soria, Cristobal

AU - Czech, Paul

AU - Despenic, Marija

AU - Garattini, Chiara

AU - Hamelinck, Dirk

AU - Heinrich, Adrienne

AU - Kraaij, Wessel

AU - Kustra, Jacek

AU - Lojo, Aizea

AU - Sanchez, Marga Martin

AU - Mayer, Miguel A.

AU - Melideo, Matteo

AU - Menasalvas, Ernestina

AU - Møller Aarestrup, Frank

AU - Artigot, Elvira Narro

AU - Petković, Milan

AU - Recupero, Diego Reforgiato

AU - Gonzalez, Alejandro Rodriguez

AU - Kerremans, Gisele Roesems

AU - Roller, Roland

AU - Romao, Mario

AU - Ruping, Stefan

AU - Sasaki, Felix

AU - Spek, Wouter

AU - Stojanovic, Nenad

AU - Thoms, Jack

AU - Vasiljevs, Andrejs

AU - Verachtert, Wilfried

AU - Wuyts, Roel

PY - 2019

Y1 - 2019

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.

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

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

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SN - 9783030052485

SP - 3

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Abedjan Z, Boujemaa N, Campbell S, Casla P, Chatterjea S, Consoli S et al. Data science in healthcare: Benefits, challenges and opportunities. In Data Science for Healthcare: Methodologies and Applications. Springer. 2019. p. 3-38 https://doi.org/10.1007/978-3-030-05249-2_1