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Big data in digital healthcare: lessons learnt and recommendations for general practice
Big 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 t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080757/ https://www.ncbi.nlm.nih.gov/pubmed/32139886 http://dx.doi.org/10.1038/s41437-020-0303-2 |
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author | Agrawal, Raag Prabakaran, Sudhakaran |
author_facet | Agrawal, Raag Prabakaran, Sudhakaran |
author_sort | Agrawal, Raag |
collection | PubMed |
description | Big 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. |
format | Online Article Text |
id | pubmed-7080757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-70807572020-03-19 Big data in digital healthcare: lessons learnt and recommendations for general practice Agrawal, Raag Prabakaran, Sudhakaran Heredity (Edinb) Review Article Big 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. Springer International Publishing 2020-03-05 2020-04 /pmc/articles/PMC7080757/ /pubmed/32139886 http://dx.doi.org/10.1038/s41437-020-0303-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Review Article Agrawal, Raag Prabakaran, Sudhakaran Big data in digital healthcare: lessons learnt and recommendations for general practice |
title | Big data in digital healthcare: lessons learnt and recommendations for general practice |
title_full | Big data in digital healthcare: lessons learnt and recommendations for general practice |
title_fullStr | Big data in digital healthcare: lessons learnt and recommendations for general practice |
title_full_unstemmed | Big data in digital healthcare: lessons learnt and recommendations for general practice |
title_short | Big data in digital healthcare: lessons learnt and recommendations for general practice |
title_sort | big data in digital healthcare: lessons learnt and recommendations for general practice |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080757/ https://www.ncbi.nlm.nih.gov/pubmed/32139886 http://dx.doi.org/10.1038/s41437-020-0303-2 |
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