Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4500
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dc.contributor.authorAgrawal, Raagen_US
dc.contributor.authorPRABAKARAN, SUDHAKARANen_US
dc.date.accessioned2020-03-20T11:22:22Z
dc.date.available2020-03-20T11:22:22Z
dc.date.issued2020-04en_US
dc.identifier.citationHeredity , 124(4), 525–534.en_US
dc.identifier.issn1365-2540en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4500-
dc.identifier.urihttps://doi.org/10.1038/s41437-020-0303-2en_US
dc.description.abstractBig Data will be an integral part of the next generation of technological developments—allowing us to gain new insights from the vast quantities of data being produced by modern life. There is significant potential for the application of Big Data to healthcare, but there are still some impediments to overcome, such as fragmentation, high costs, and questions around data ownership. Envisioning a future role for Big Data within the digital healthcare context means balancing the benefits of improving patient outcomes with the potential pitfalls of increasing physician burnout due to poor implementation leading to added complexity. Oncology, the field where Big Data collection and utilization got a heard start with programs like TCGA and the Cancer Moon Shot, provides an instructive example as we see different perspectives provided by the United States (US), the United Kingdom (UK) and other nations in the implementation of Big Data in patient care with regards to their centralization and regulatory approach to data. By drawing upon global approaches, we propose recommendations for guidelines and regulations of data use in healthcare centering on the creation of a unique global patient ID that can integrate data from a variety of healthcare providers. In addition, we expand upon the topic by discussing potential pitfalls to Big Data such as the lack of diversity in Big Data research, and the security and transparency risks posed by machine learning algorithms.en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.subjectBiologyen_US
dc.subjectTOC-MAR-2020en_US
dc.subject2020en_US
dc.subject2020-MAR-WEEK3en_US
dc.titleBig data in digital healthcare: lessons learnt and recommendations for general practiceen_US
dc.typeArticleen_US
dc.contributor.departmentDept. of Biologyen_US
dc.identifier.sourcetitleHeredityen_US
dc.publication.originofpublisherForeignen_US
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