Artificial Intelligence and Machine Learning Integration in Canadian Healthcare Decision-Making: A Management Framework for Clinical and Operational Applications

Abstract

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming healthcare systems globally by enabling the analysis of extensive medical and administrative data to detect patterns, generate predictions, and facilitate evidence-driven decision-making. In Canadian healthcare settings, AI technologies offer significant potential for supporting clinical diagnosis, predicting patient risks, optimizing resource allocation, and enhancing operational efficiency. This paper examines the research question: In what ways can artificial intelligence and machine learning technologies be successfully incorporated into decision-making processes of Canadian healthcare organizations to enhance clinical outcomes and operational efficiency while addressing ethical and implementation challenges? Through a comprehensive literature review, this study evaluates the applications of AI in clinical decision support systems, predictive analytics, and administrative operations within the Canadian healthcare context. Key findings indicate that while AI offers substantial benefits for pattern recognition, risk prediction, and operational planning, implementation requires careful attention to governance frameworks, algorithmic fairness, data privacy protections, and integration with existing clinical workflows. The study identifies critical challenges including data fragmentation across provincial jurisdictions, workforce readiness, and the need for robust health technology assessment processes. Strategic recommendations for healthcare managers include establishing clear AI governance policies, investing in workforce training, ensuring diverse and representative datasets, and developing collaborative implementation approaches that maintain clinical judgment as the foundation of patient care (Pirouzi, 2026).

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