Connecting data to insight: a pan-canadian study on AI in healthcare
Abstract
Across Canada, healthcare leaders are exploring the potential of artificial intelligence and advanced analytics to transform the healthcare system. This report shares a summary of the current state of healthcare analytics across major hospitals and public healthcare agencies in Canada. We present information on the current level of investment, data governance maturity, analytics talent and tools and models being leveraged across the nation. The findings point to an opportunity for enhanced collaboration in advanced analytics and the adoption of nascent artificial intelligence technologies in healthcare. The recommendations will help drive adoption in Canada, ultimately improving the patient experience and promoting better health outcomes for Canadians.
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Algorithms trained on biased data often replicate and magnify inequalities present in their source material, disproportionately disadvantaging marginalized groups.
“Algorithms trained on biased data often replicate and magnify the inequalities present in their source material, disproportionately disadvantaging marginalized groups [[6], [7], [8]].”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [7]
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Amaya-Santos, S., Vargas, C., & Bermúdez-Tamayo, C. (2026) · Public Health in Practice · doi:10.1016/j.puhip.2025.100713 · Cited by 4 · see citing works
“Observed sex and gender differences in health and well-being are influenced by complex biological, socio-economic and context factors, often confounded by variables such as stigma, stereotypes, and data misrepresentation [7].”
- Authors
- Hakim, Z., Ierasts, T., Hakim, I., D'Penha, A., Smith, K., & Caesar, M.
- Affiliations
- University of Toronto (CA)
- University Health Network (CA)
- McMaster University (CA)
- ORCID
- Hakim, Zaki
- Venue
- Healthcare Quarterly
- Publisher
- Longwoods Publishing
- Published
- 2020-04-30
- Volume
- 23
- Issue
- 1
- Pages
- 13-19
- ISSN
- 1710-2774 (ISSN-L)
- OpenAlex
- W3015088231
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 2 · 2024: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Healthcare Quarterly (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- artificial intelligence in healthcare, medical data analysis, data governance, artificial intelligence adoption, hospital data, patient experience, health outcomes, Canada
- MeSH terms
- Data Management, Artificial Intelligence, Delivery of Health Care, Hospital Administration, Hospitals, Humans, Surveys and Questionnaires
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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