Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study
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.
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.
A total of 4 themes were identified:
1. AI was described as the inevitable future, facilitating more efficient, accessible social determinants of health information and use in primary care.
2. Participants expressed concerns about potential health care harms and a distrust in AI and public systems.
3. 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.
4. Participants described the critical importance of consent and the need for strong safeguards to protect patient data and trust.
This study was conducted as part of a multicomponent project that developed and refined a standardized SDoH questionnaire for primary care settings, known as the Screening for Poverty And Related Social Determinants and Intervening to Improve Knowledge of and Links to Resources (SPARK) Tool.
Published in the Journal of Medical Internet Research
