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Cited by 1,522Thoreau and American LiteratureEthics in Clinical ResearchArtificial Intelligence in Healthcare and Education

Privacy in the age of medical big data

Open access
Price, W. Nicholson, Cohen, I. Glenn·2019·Nature Medicine
DOI 10.1038/s41591-018-0272-7PMC PMC6376961
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Abstract

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system. The increased amount of health care data collected brings with it ethical and legal challenges for protecting the patient while optimizing health care and research.

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Crossref · 10.1038/s41591-018-0272-7

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  • The HIPAA Privacy Rule is the key federal law protecting health data privacy in the United States.

    “The Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule (45 C.F.R. Part 160 as well as subparts A and E of Part 164) is the key federal law to protect health data privacy ([94], p. 38).”

    From Ethical and legal challenges of artificial intelligence-driven healthcare (2020) · cites this paper as [94]

  • HIPAA has significant gaps in today's healthcare environment because it covers only specific health information generated by covered entities or their business associates.

    “However, HIPAA has significant gaps when it comes to today’s healthcare environment since it only covers specific health information generated by “covered entities” or their “business associates.””

    From Ethical and legal challenges of artificial intelligence-driven healthcare (2020) · cites this paper as [94]

  • HIPAA does not apply to nonhealth information that supports inferences about health, such as purchasing a pregnancy test on Amazon.

    “HIPAA does not apply to nonhealth information that supports inferences about health such as a purchase of a pregnancy test on Amazon ([95], p. 232; [94], p. 39).”

    From Ethical and legal challenges of artificial intelligence-driven healthcare (2020) · cites this paper as [94]

  • HIPAA does not apply to user-generated health information.

    “HIPAA also does not apply in cases of user-generated health information ([95], p. 232; [94], p. 39).”

    From Ethical and legal challenges of artificial intelligence-driven healthcare (2020) · cites this paper as [94]

  • HIPAA de-identification may not adequately protect patients because data triangulation can re-identify data thought to be de-identified by combining multiple datasets.

    “But this may not adequately protect patients because of the possibility of data triangulation—to re-identify data thought to be de-identified under the statute through the combination of multiple datasets ([94], pp. 39, 40; [96]).”

    From Ethical and legal challenges of artificial intelligence-driven healthcare (2020) · cites this paper as [94]

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Research works printed but not linked: 8

  1. [4]Institute of Medicine. Committee on Quality of Health Care in America, the National Academies. To Err is Human: Building a Safer Health System (eds. Kohn LT, Corrigan JM, & Donaldson MS, National Academies Press, Washington, D.C., 2000). 25077248
  2. [16]Institute of Medicine. The Learning Healthcare System: Workshop Summary (eds. Olsen LA, Aisner D & McGinnis JM, National Academies Press, Washington, D.C., 2007). 21452449
  3. [41]Malanga SE, Loe JD, Robertson CT & Ramos KS Who’s Left Out of Big Data? How Big Data Collection, Analysis, and Use Neglects Populations Most in Need of Medical and Public Health Research and Interventions, in Big Data, Health Law, and Bioethics (eds. Cohen IG, Fernandez Lynch H., Vayena E & Gasser U, Cambridge Univ. Press, New York, 2018).
  4. [45]Cohen IG Is there a duty to share health care data?, in Big Data, Health Law, and Bioethics (eds. Cohen IG, Fernandez Lynch H., Vayena E & Gasser U, Cambridge Univ. Press, New York, 2018).
  5. [58]Hoffman S Big Data’s New Discrimination Threats: Amending the Americans with Disabilities Act to Cover Discrimination Based on Data-Driven Predictions of Future Disease, in Big Data, Health Law, and Bioethics (eds. Cohen IG, Fernandez Lynch H., Vayena E & Gasser U, Cambridge Univ. Press, New York, 2018).
  6. [64]Skopek JM Big Data’s Epistemology and Its Implications for Precision Medicine and Privacy, in Big Data, Health Law, and Bioethics (eds. Cohen IG, Fernandez Lynch H., Vayena E & Gasser U, Cambridge Univ. Press, New York, 2018).
  7. [67]Price WN II Drug approval in a learning health system Preprint at https://papers.ssrn.com/abstract_id=3152570.
  8. [76]Narayanan A & Shmatikov V Robust deanonymization of large sparse datasets (how to break the anonymity of the Netflix prize database). IEEE Sec’y & Privacy Symposium (2008), available at http://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf.

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Authors
Price, W. N., & Cohen, I. G.
Affiliations
University of Copenhagen (DK)
Corresponding author
Cohen, I. Glenn
ORCID
Venue
Nature Medicine
Publisher
Springer Science and Business Media LLC
Published
2019-01
Volume
25
Issue
1
Pages
37-43
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
DOI
10.1038/s41591-018-0272-7
PMC
PMC6376961
OpenAlex
W2903777941
Semantic Scholar
9e3816be8cf4821d74e258de10ee471382936a30
Cited by
1,522
References
64
Citations by year
2026: 316 · 2025: 334 · 2024: 246 · 2023: 223 · 2022: 151 · 2021: 131 · 2020: 92 · 2019: 27 · 2018: 1
FWCI
11.56
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • bepress Legal Repository (submittedVersion)
Funders
  • Novo Nordisk Fonden (NNF17SA0027784)
  • National Cancer Institute (R01CA214829)
Topics
  • Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
patient privacy, health data, big data in healthcare, data protection law, health data ethics, informed consent, equity, discrimination, data breaches, health data regulation, artificial intelligence in healthcare, machine learning in medicine
MeSH terms
Big Data, Delivery of Health Care, Humans, United States, Privacy, Health Insurance Portability and Accountability Act

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