Big data in digital healthcare: lessons learnt and recommendations for general practice
Open accessAbstract
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.
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Digital technologies may support clinical management of depression by capturing longitudinal, temporally dense, and multimodal mental health data for use in diagnosis and monitoring; analyzing data using ML paradigms to generate personalized and clinically actionable insights and predictions; and supporting integrated care by facilitating connections to clinical care, peer support, personalized资源,
“Besides therapeutic interventions, digital technologies may support clinical management of depression by: (a) capturing longitudinal, temporally dense, and multimodal mental health data for use in diagnosis and monitoring; (b) analyzing data using ML paradigms to generate personalized and clinically actionable insights and predictions; and (c) supporting integrated care by facilitating connections”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [70]
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- Authors
- Agrawal, R., & Prabakaran, S.
- Affiliations
- University of Cambridge (GB)
- Indian Institute of Science Education and Research Pune (IN)
- Corresponding author
- Prabakaran, Sudhakaran
- Venue
- Heredity
- Published
- 2020-03-05
- Volume
- 124
- Issue
- 4
- Pages
- 525-534
- ISSN
- 0018-067X (ISSN-L)
- 1365-2540
- PMC
- PMC7080757
- OpenAlex
- W3010611814
- Cited by
- 218
- References
- 50
- Citations by year
- 2026: 14 · 2025: 27 · 2024: 34 · 2023: 68 · 2022: 35 · 2021: 35 · 2020: 5
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Heredity (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- big data, digital healthcare, general practice, data ownership, data fragmentation, physician burnout, TCGA, data regulation, data integration, diversity in research, machine learning security, transparency
- MeSH terms
- Machine Learning, Big Data, Delivery of Health Care, United Kingdom, Humans, United States, General Practice
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