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

Isabelle Rose I Alberto; Nicole Rose I Alberto; Arnab K Ghosh; Bhav Jain; Shruti Jayakumar; Nicole Martinez-Martin; Ned McCague; Dana Moukheiber; Lama Moukheiber; Mira Moukheiber; Sulaiman Moukheiber; Antonio Yaghy; Andrew Zhang; Leo Anthony Celi

doi:10.1016/S2589-7500(23)00025-0

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

Lancet Digit Health. 2023 May;5(5):e288-e294. doi: 10.1016/S2589-7500(23)00025-0.

Affiliations

¹ College of Medicine, University of the Philippines, Manila, Philippines.
² Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY, USA.
³ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁴ Deloitte Consulting, London, UK.
⁵ Stanford Center for Biomedical Ethics, Stanford Medicine, Stanford, CA, USA.
⁶ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Markforged, Watertown, MA, USA.
⁷ The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁸ Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.
⁹ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; New England Eye Center, Tufts University Medical Center, Boston, MA, USA.
¹⁰ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
¹¹ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA. Electronic address: lceli@bidmc.harvard.edu.

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.

Publication types

Research Support, N.I.H., Extramural
Review

MeSH terms

Algorithms*
Biomedical Research*
Consumer Health Information / economics
Consumer Health Information / ethics
Datasets as Topic* / economics
Datasets as Topic* / ethics
Datasets as Topic* / trends
Humans
Privacy
Reproducibility of Results

Grants and funding

R01 EB017205/EB/NIBIB NIH HHS/United States