Big Data and Clinicians: A Review on the State of the Science

Wang, Weiqi; Krishnan, Eswar

doi:10.2196/medinform.2913

Cited by 121 publications

(73 citation statements)

References 85 publications

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“…17 At present, the term is more and more used to refer to the technical or analytical methods to extract information from complex or multiple data sets. 1,18 Big Data sources potentially valuable to medical researchers include electronic health records (EHRs), 19 aggregated clinical trial data, administrative health care, 20 and genomic and other -omics data. 1,21 Nowadays, online activities of individuals, for example on mobile phones, 22 also allow the continuous collection of health-related and other data.…”

Section: The Context Of Data-intensive Medical Researchmentioning

confidence: 99%

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

et al. 2015

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Medical research is increasingly becoming data-intensive; sensitive data are being re-used, linked and analysed on an unprecedented scale. The current EU data protection law reform has led to an intense debate about its potential effect on this processing of data in medical research. To contribute to this evolving debate, this paper reviews how the dominant 'consent or anonymise approach' is challenged in a data-intensive medical research context, and discusses possible ways forwards within the EU legal framework on data protection. A large part of the debate in literature focuses on the acceptability of adapting consent or anonymisation mechanisms to overcome the challenges within these approaches. We however believe that the search for ways forward within the consent or anonymise paradigm will become increasingly difficult. Therefore, we underline the necessity of an appropriate research exemption from consent for the use of sensitive personal data in medical research to take account of all legitimate interests. The appropriate conditions of such a research exemption are however subject to debate, and we expect that there will be minimal harmonisation of these conditions in the forthcoming EU Data Protection Regulation. Further deliberation is required to determine when a shift away from consent as a legal basis is necessary and proportional in a data-intensive medical research context, and what safeguards should be put in place when such a research exemption from consent is provided.

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Section: The Context Of Data-intensive Medical Researchmentioning

confidence: 99%

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

et al. 2015

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“…Wang and Krishnan (2014) also provide an extensively referenced review of specific Big Data techniques used in clinical medicine. Interested readers are referred to these manuscripts for detailed citations; here our goal is to provide an overview.…”

Section: Big Data As New Analytic Methodsmentioning

confidence: 99%

What’s So Different about Big Data?. A Primer for Clinicians Trained to Think Epidemiologically

Iwashyna

Liu

2014

Annals ATS

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The Big Data movement in computer science has brought dramatic changes in what counts as data, how those data are analyzed, and what can be done with those data. Although increasingly pervasive in the business world, it has only recently begun to influence clinical research and practice. As Big Data draws from different intellectual traditions than clinical epidemiology, the ideas may be less familiar to practicing clinicians. There is an increasing role of Big Data in health care, and it has tremendous potential. This Demystifying Data Seminar identifies four main strands in Big Data relevant to health care. The first is the inclusion of many new kinds of data elements into clinical research and operations, in a volume not previously routinely used. Second, Big Data asks different kinds of questions of data and emphasizes the usefulness of analyses that are explicitly associational but not causal. Third, Big Data brings new analytic approaches to bear on these questions. And fourth, Big Data embodies a new set of aspirations for a breaking down of distinctions between research data and operational data and their merging into a continuously learning health system.

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“…Also, the genome project has stimulated the development of advanced technology to characterize DNA and study genes. Several initiatives for the development of infrastructures for genomic data sharing have been born since, to allow exchange of empirical data but also facilitate analysis of clinical situations (Merelli, Perez-Sanchez, Gesing, & D'Agostino, 2014;Ray, 2015;Staes et al, 2009;Wang & Krishnan, 2014). A crucial spinoff of these activities has been the development and routinization of specific and reliable diagnostic tests, which in a (relatively limited) number of cases have considerably shortened the process it takes to come to a reliable diagnosis (Eisenstein, 2014;Keating & Cambrosio, 2013;Khoury, Evans, & Burke, 2010).…”

Section: Key Conceptsmentioning

confidence: 99%

“…The importance of GDB in this process exemplifies how the availability of well-curated, comprehensive phenotype-oriented mapping and annotation databases, providing not only gene maps but mapping and application data for clinical research, is crucial to the fruitful translation of genome knowledge into practical applications. Indeed, data infrastructure initiatives have flourished over the last decade, encompassing a wide spectrum of genomic data-based services, from general repositories to support for the analysis of individual cases (Bin Han Ong, 2015;Merelli et al, 2014;Staes et al, 2009;Wang & Krishnan, 2014). At the same time, the demise of GDB in 2008 highlights how databases and related infrastructures have unclear F o r R e v i e w O n l y sustainability and longevity, as their operational costs are not shared across the community in a proportioned way and their adoption is not unanimous (Ribes & Bowker, 2009;Ure et al, 2009;Bastow & Leonelli, 2010).…”

Section: Sequencing and Data Infrastructuresmentioning

confidence: 99%

Human Genome Project, Personalised Medicine and Future Health Care

Tempini

Leonelli

2015

Encyclopedia of Life Sciences

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Abstract:At the turn of the millennium, the Human Genome Project and the upcoming publication of the human genome sequence promised to open an entirely new approach to healthcare, based on the genotype of the individual. This approach was dubbed personalised medicine (PM). However, the analysis of sequencing results revealed that the complexity of the biological world had been underestimated. The major project of revolutionizing medicine through genomics requires a more sophisticated and multilevel understanding of living systems, which in turn demands new data, models and modes of intervention on humans as well as non-human organisms. Thus, the most advanced applications of PM involve a complex interweaving of biological and medical knowledge, as well as increasing attention to the technical systems through which data about any specific individual could be processed. Further, the development of PM needs to include consideration of several key ethical issues, ranging from privacy and data control to the risk that dependence on sophisticated technologies will widen the gap between haves and have-nots both globally and within any one country. AbstractAt the turn of the millennium, the Human Genome Project and the upcoming publication of the human genome sequence promised to open an entirely new approach to healthcare, based on the genotype of the individual. This approach was dubbed personalised medicine (PM). However, the analysis of sequencing results revealed that the complexity of the biological world had been underestimated. The major project of revolutionizing medicine through genomics requires a more sophisticated and multilevel understanding of living systems, which in turn demands new data, models and modes of intervention on humans as well as nonhuman organisms. Thus, the most advanced applications of PM involve a complex interweaving of biological and medical knowledge, as well as increasing attention to the technical systems through which data about any Keywords: human genome project; healthcare; personalised medicine; precision medicine; pharmacogenomics; data-intensive science; translational medicine. Key Concepts-Personalised medicine describes the direction medical solutions are expected to evolve, towards personalisation and individual tailoring of therapies and treatment regimes that will cut and divide through patient populations. The motor of this promised innovation will be genomic profiling techniques, of nuclear DNA but also ones of latter development such as proteomics, metabolomics, transcriptomics, and so on.-Biomedicine is an interdisciplinary space involving biological and medical knowledge/expertise as well as IT, where scientific knowledge about biological phenomena is mobilized in order to devise solutions to medical problems.-Data-intensive science describes the mode of scientific research that is emerging as paramount in an age characterised by the increasing reliance and dependence of researchers on the development of complex, distributed infrastructures for data sharing and anal...

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Big Data and Clinicians: A Review on the State of the Science

Cited by 121 publications

References 85 publications

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

What’s So Different about Big Data?. A Primer for Clinicians Trained to Think Epidemiologically

Human Genome Project, Personalised Medicine and Future Health Care

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