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Cited by 218Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsBig Data Technologies and Applications

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

Big data in digital healthcare: lessons learnt and recommendations for general practice

Open access
Agrawal, Raag, Prabakaran, Sudhakaran·2020·Heredity
DOI 10.1038/s41437-020-0303-2PMC PMC7080757
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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.

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Fig. 1 The promise of Big Data is in its ability to prevent disease not just help doctors diagnose them. In this example we demonstrate how many possible factors may come together to better target patients for early screening measures, which can lower aggregate costs for the healthcare system.
Fig. 1 The promise of Big Data is in its ability to prevent disease not just help doctors diagnose them. In this example we demonstrate how many possible factors may come together to better target patients for early screening measures, which can lower aggregate costs for the healthcare system.CC BY · PubMed Central
Fig. 2 General model of care envisioned. Here, various heterogeneous data types are fed into a centralized EHR system that will be uploaded to a secure digital cloud where it can be de-identified and used by research and enterprise, but primarily by physicians and patients.
Fig. 2 General model of care envisioned. Here, various heterogeneous data types are fed into a centralized EHR system that will be uploaded to a secure digital cloud where it can be de-identified and used by research and enterprise, but primarily by physicians and patients.CC BY · PubMed Central
Fig. 3 The need for patient data protection is of great concern. Future implementations of Big Data will need to not only integrate data, but also encrypt and de-identify it for secure storage.
Fig. 3 The need for patient data protection is of great concern. Future implementations of Big Data will need to not only integrate data, but also encrypt and de-identify it for secure storage.CC BY · PubMed Central
Fig. 4 Our recommended healthcare system model. Hypothetical healthcare system design based on unique patient identifiers that function across a variety of systems and providers—linking together disparate datasets into a complete patient profile.
Fig. 4 Our recommended healthcare system model. Hypothetical healthcare system design based on unique patient identifiers that function across a variety of systems and providers—linking together disparate datasets into a complete patient profile.CC BY · PubMed Central
Fig. 5 This is an undesirable model for healthcare that can be implemented if regulators are not vigilant. Without transparency in many of the models being implemented as to why and how decisions are being made, there exists room for algorithmic bias and no room for improvement or criticism by physicians. The “black box” of machine learning obscures why decisions are made and what actually affects predictions.
Fig. 5 This is an undesirable model for healthcare that can be implemented if regulators are not vigilant. Without transparency in many of the models being implemented as to why and how decisions are being made, there exists room for algorithmic bias and no room for improvement or criticism by physicians. The “black box” of machine learning obscures why decisions are made and what actually affects predictions.CC BY · PubMed Central
Fig. 6 Minority representation in current large-scale experiments integrating across a variety of factors is often lacking. The “All of Us” study will meet this need by specifically aiming to recruit a diverse pool of participants to develop disease models that generalize to every citizen, not just the majority (Denny et al. 2019). Future global Big Data generation projects should learn from this example in order to guarantee equality of care for all patients.
Fig. 6 Minority representation in current large-scale experiments integrating across a variety of factors is often lacking. The “All of Us” study will meet this need by specifically aiming to recruit a diverse pool of participants to develop disease models that generalize to every citizen, not just the majority (Denny et al. 2019). Future global Big Data generation projects should learn from this example in order to guarantee equality of care for all patients.CC BY · PubMed Central

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Metadata

Authors
Agrawal, R., & Prabakaran, S.
Affiliations
  • University of Cambridge (GB)
  • Indian Institute of Science Education and Research Pune (IN)
Corresponding author
Prabakaran, Sudhakaran
ORCID
Venue
Heredity
Publisher
Springer Science and Business Media LLC
Published
2020-03-05
Volume
124
Issue
4
Pages
525-534
ISSN
  • 0018-067X (ISSN-L)
  • 1365-2540
DOI
10.1038/s41437-020-0303-2
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
Licence
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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