@article{1a402649e7a149c69ee470bbed2ede03,
title = "Mitigating algorithmic bias in opioid risk-score modeling to ensure equitable access to pain relief",
author = "Bhagwat, {Atharva M.} and Ferryman, {Kadija S.} and Gibbons, {Jason B.}",
note = "Funding Information: 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. ",
year = "2023",
month = apr,
doi = "10.1038/s41591-023-02256-0",
language = "English (US)",
volume = "29",
pages = "769--770",
journal = "Nature medicine",
issn = "1078-8956",
publisher = "Nature Research",
number = "4",
}