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Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study

Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study

Participant demographics (N=195; adapted from Adekoya et al [43], which is published under Creative Commons Attribution 4.0 International License [51]). a Percentage values are not provided for values that are ≤5. b Race and ethnicity data were not collected in Manitoba. c Unmet social needs were based on the following categories: precarious employment (presence of all of the following: short term, casual, or temporary employment; fear of being fired if raised employment concerns; and varying pay); living in social

Victoria H Davis, Jinfan Rose Qiang, Itunuoluwa Adekoya MacCarthy, Dana Howse, Abigail Zita Seshie, Leanne Kosowan, Alannah Delahunty-Pike, Eunice Abaga, Jane Cooney, Marjeiry Robinson, Dorothy Senior, Alexander Zsager, Kris Aubrey-Bassler, Mandi Irwin, Lois A Jackson, Alan Katz, Emily Gard Marshall, Nazeem Muhajarine, Cory Neudorf, Stephanie Garies, Andrew D Pinto

J Med Internet Res 2025;27:e52244