Big Data, Big Tech, and Protecting Patient Privacy
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
De-identification of wearable data may be insufficient because meta-data associated with the user can theoretically be used to re-identify them.
“Although de-identification is a possibility, the meta-data associated with the user can be theoretically used to re-identify them110.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [110]
Next-generation cybersecurity tools such as blockchain have been proposed as a means for secure storage and prevention of data breaches.
“Nextgeneration cybersecurity tools such as block chain have been proposed as a means for secure storage and prevention of such data breaches.56”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [56]
Cites
Privacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522 · see citing works
Self-citationTai‐Seale, M., Dillon, E. C., Yan, Y., Nordgren, R., Steinberg, R. L., Nauenberg, T., Lee, T. C., Meehan, A. E., Li, J., Chan, A. S., et al. (2019) · Health Affairs · doi:10.1377/hlthaff.2018.05509 · Cited by 225 · see citing works
Health Research with Big Data: Time for Systemic Oversight
Vayena, E., & Blasimme, A. (2018) · The Journal of Law Medicine & Ethics · doi:10.1177/1073110518766026 · Cited by 180 · see citing works
A New Model for Industry–Academic Partnerships
King, G., & Persily, N. (2019) · PS Political Science & Politics · doi:10.1017/s1049096519001021 · Cited by 165 · see citing works
A Belmont Report for Health Data
Parasidis, E., Pike, E. R., & McGraw, D. (2019) · New England Journal of Medicine · doi:10.1056/nejmp1816373 · Cited by 36 · see citing works
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Smart wearable devices in cardiovascular care: Where we are and how to move forward
Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00522-7 · Cited by 829 · see citing works
Axes of a revolution: challenges and promises of big data in healthcare
Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370 · see citing works
Carter, S. M., Rogers, W., Win, K. T., Frazer, H., Richards, B., & Houssami, N. (2020) · The Breast · doi:10.1016/j.breast.2019.10.001 · Cited by 312 · see citing works
Thoral, P. J., Peppink, J. M., Driessen, R. H., Sijbrands, E. J. G., Kompanje, E. J. O., Kaplan, L., Bailey, H., Kesecioglu, J., Cecconi, M., Churpek, M., et al. (2021) · Critical Care Medicine · doi:10.1097/ccm.0000000000004916 · Cited by 257 · see citing works
A Path for Translation of Machine Learning Products into Healthcare Delivery
(2020) · EMJ Innovations · doi:10.33590/emjinnov/19-00172 · Cited by 172 · see citing works
“106 Second, the dataset used to train a model 107 or the outcome that a model predicts 108 can have significant implications for how models lessen or worsen disparities in healthcare.”
MethodEthical and Legal Implications of Remote Monitoring of Medical Devices
COHEN, I. G., GERKE, S., & KRAMER, D. B. (2020) · The Milbank Quarterly · doi:10.1111/1468-0009.12481 · Cited by 65 · see citing works
Self-citationMarvasti, T. B., Gao, Y., Murray, K. R., Hershman, S., McIntosh, C., & Moayedi, Y. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.009 · Cited by 35 · see citing works
On patient safety: the lure of artificial intelligence—are we jeopardizing our patients' privacy?
Rickert, J. (2020) · Clinical Orthopaedics & Related Research · doi:10.1097/corr.0000000000001189 · Cited by 21 · see citing works
HIPAA in the Era of Data Sharing
Kels, C. G. (2020) · JAMA · doi:10.1001/jama.2019.19645 · Cited by 15 · see citing works
- Authors
- Cohen, I. G., & Mello, M. M.
- Affiliations
- Harvard University (US)
- Stanford Medicine (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2019-09-24
- Volume
- 322
- Issue
- 12
- Pages
- 1141
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2969107720
- Cited by
- 135
- References
- 5
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 19 · 2023: 26 · 2022: 23 · 2021: 16 · 2020: 25 · 2019: 3
- FWCI
- 20.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Novo Nordisk Fonden (NNF17SA0027784)
- Topics
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- big data, Big Tech, patient privacy, health data privacy, data protection, health information technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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