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Cited by 86Research Data Management PracticesArtificial Intelligence in Healthcare and EducationElectronic Health Records Systems

The impact of commercial health datasets on medical research and health-care algorithms

Open access
Alberto, Isabelle Rose I, Alberto, Nicole Rose I, Ghosh, Arnab K, Jain, Bhav, Jayakumar, Shruti, Martinez-Martin, Nicole, McCague, Ned, Moukheiber, Dana +6 more·2023·The Lancet Digital Health
DOI 10.1016/s2589-7500(23)00025-0PMC PMC10155113
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Abstract

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

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  • Many healthcare institutions, especially those smaller or with constrained data and computational resources, increasingly seek to leverage models developed at facilities with greater data resources.

    “As a remedy, many healthcare institutions, especially those smaller or with constrained data and computational resources, increasingly seek to leverage models developed at facilities with greater data resources [15].”

    From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [15]

    The section on reproducibility and generalisability reports that the Epic Sepsis Model, developed using data from three hospital systems, was implemented at hundreds of US hospitals. It does not say that smaller or resource-constrained institutions increasingly seek such models, or that resource constraints motivate their adoption; it attributes this model’s widespread use to ease of EHR integration. (LLM, conf=0.80)

    Experimental — accuracy not yet measured

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Authors
Alberto, I. R. I., Alberto, N. R. I., Ghosh, A. K., Jain, B., Jayakumar, S., Martinez-Martin, N., McCague, N., Moukheiber, D., Moukheiber, L., Moukheiber, M., Moukheiber, S., Yaghy, A., Zhang, A., & Celi, L. A.
Affiliations
  • University of the Philippines Manila (PH)
  • Cornell University (US)
  • Massachusetts Institute of Technology (US)
  • Deloitte (United Kingdom) (GB)
  • Worcester Polytechnic Institute (US)
  • Tufts University (US)
  • Harvard University (US)
  • Beth Israel Deaconess Medical Center (US)
ORCID
Venue
The Lancet Digital Health
Publisher
Elsevier BV
Published
2023-05
Volume
5
Issue
5
Pages
e288-e294
ISSN
2589-7500 (ISSN-L)
DOI
10.1016/s2589-7500(23)00025-0
PMC
PMC10155113
OpenAlex
W4366818978
Cited by
86
References
11
Citations by year
2026: 17 · 2025: 33 · 2024: 30 · 2023: 6
FWCI
3.55
Type
journal article
Language
en
Open access
Licence
Locations
  • The Lancet Digital Health (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • Greater South Information System
Funders
National Institutes of Health (EB017205)
Topics
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
data reproducibility, generalizability, data ethics, data curation, data privacy, digital health
MeSH terms
Algorithms, Humans, Reproducibility of Results, Privacy, Biomedical Research, Consumer Health Information, Datasets as Topic

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