Abstract
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.
Highlights
The advent of Generation Sequencing promises to revolutionize medicine as it has become possible to cheaply and reliably sequence entire genomes, transcriptomes, proteomes, metabolomes, etc. (Shendure and Ji 2008; Topol 2019a)
“Genomical” data alone is predicted to be in the range of 2–40 Exabytes by 2025—eclipsing the amount of Associate editor: Frank Hailer
In addition to the reductions in cost of sequencing strategies, computational power, and storage have become extremely cheap. All these developments have brought enormous advances in disease diagnosis and treatments, they have introduced new challenges as large-scale information becomes increasingly difficult to store, analyze, and interpret (Adibuzzaman et al 2018). This problem has given way to a new era of “Big Data” in which scientists across a variety of fields are exploring new ways to understand the large amounts of unstructured and unlinked data generated by modern technologies, and leveraging it to discover new knowledge (Krumholz 2014; Fessele 2018)
Summary
The advent of Generation Sequencing promises to revolutionize medicine as it has become possible to cheaply and reliably sequence entire genomes, transcriptomes, proteomes, metabolomes, etc. (Shendure and Ji 2008; Topol 2019a). Its data sources are valuable for scientists, as each patient’s entries are linked to unique identity numbers that can be cross references with over 90 other registries to give a more complete understanding of a patient’s health and social circumstances These registries are not limited to disease states and treatments, and encompass extensive public administrative records that can provide researchers considerable insight into social indicators of health such as income, occupation, and marital status (Connelly et al 2016). Sweden and China are adopting this model—leveraging unique identity numbers issued to citizens to link otherwise disconnected datasets from administrative and healthcare records (Connelly et al 2016; Cnudde et al 2016; Zhang et al 2018) In this way, tests, technologies and methods will be integrated in a way that is specific to the patient but not necessarily to the hospital or clinic. Advantages of blockchain for healthcare data transfer and storage lie in its security and privacy, but the approach has yet to gain widespread use
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