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Mitigating algorithmic bias in opioid risk-score modeling to ensure equitable access to pain relief

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Acknowledgements

We thank J.M. Sharfstein for reviewing and providing feedback on this manuscript. We also thank R. Gibbons for helpful discussions on evidence-based screening tools, and M. Vogel for insight on ethics surrounding artificial intelligence and algorithms in healthcare. The views, opinions, and findings in this article are those of the authors. This project was funded through a grant from Bloomberg Philanthropies. The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Authors and Affiliations

  1. Johns Hopkins University, Baltimore, MD, USA

    Atharva M. Bhagwat

  2. Department of Health Policy & Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

    Atharva M. Bhagwat, Kadija S. Ferryman & Jason B. Gibbons

  3. Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, USA

    Kadija S. Ferryman

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  1. Atharva M. Bhagwat
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  2. Kadija S. Ferryman
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  3. Jason B. Gibbons
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Corresponding author

Correspondence to Jason B. Gibbons.

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Competing interests

K.S.F. is a member of the digital ethics advisory board for Merck and of the institutional review board for the US National Institutes of Health’s All of Us Research Program. The other authors declare no competing interests.

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Bhagwat, A.M., Ferryman, K.S. & Gibbons, J.B. Mitigating algorithmic bias in opioid risk-score modeling to ensure equitable access to pain relief. Nat Med 29, 769–770 (2023). https://doi.org/10.1038/s41591-023-02256-0

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  • DOI: https://doi.org/10.1038/s41591-023-02256-0

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