%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e52244
%T Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study
%A Davis,Victoria H
%A Qiang,Jinfan Rose
%A Adekoya MacCarthy,Itunuoluwa
%A Howse,Dana
%A Seshie,Abigail Zita
%A Kosowan,Leanne
%A Delahunty-Pike,Alannah
%A Abaga,Eunice
%A Cooney,Jane
%A Robinson,Marjeiry
%A Senior,Dorothy
%A Zsager,Alexander
%A Aubrey-Bassler,Kris
%A Irwin,Mandi
%A Jackson,Lois A
%A Katz,Alan
%A Marshall,Emily Gard
%A Muhajarine,Nazeem
%A Neudorf,Cory
%A Garies,Stephanie
%A Pinto,Andrew D
%+ Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada, 1 416 864 6060 ext 76148, andrew.pinto@utoronto.ca
%K artificial intelligence
%K social determinants of health
%K sociodemographic data
%K social needs
%K social care
%K primary care
%K machine learning
%K qualitative study
%D 2025
%7 6.3.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Data on the social determinants of health could be used to improve care, support quality improvement initiatives, and track progress toward health equity. However, this data collection is not widespread. Artificial intelligence (AI), specifically natural language processing and machine learning, could be used to derive social determinants of health data from electronic medical records. This could reduce the time and resources required to obtain social determinants of health data. Objective: This study aimed to understand perspectives of a diverse sample of Canadians on the use of AI to derive social determinants of health information from electronic medical record data, including benefits and concerns. Methods: Using a qualitative description approach, in-depth interviews were conducted with 195 participants purposefully recruited from Ontario, Newfoundland and Labrador, Manitoba, and Saskatchewan. Transcripts were analyzed using an inductive and deductive content analysis. Results: A total of 4 themes were identified. First, AI was described as the inevitable future, facilitating more efficient, accessible social determinants of health information and use in primary care. Second, participants expressed concerns about potential health care harms and a distrust in AI and public systems. Third, some participants indicated that AI could lead to a loss of the human touch in health care, emphasizing a preference for strong relationships with providers and individualized care. Fourth, participants described the critical importance of consent and the need for strong safeguards to protect patient data and trust. Conclusions: These findings provide important considerations for the use of AI in health care, and particularly when health care administrators and decision makers seek to derive social determinants of health data. 
%M 40053728
%R 10.2196/52244
%U https://www.jmir.org/2025/1/e52244
%U https://doi.org/10.2196/52244
%U http://www.ncbi.nlm.nih.gov/pubmed/40053728