BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) capabilities and increased access to methods have enabled population-level analysis for research and health system monitoring. However, governments and public authorities may not be aware of the full range of applications, opportunities, and barriers and challenges. The purpose of the present study was to understand the barriers and facilitators of AI/ML and whether (and how) AI/ML was being used (or could be used) in public sector health organisations in a publicly funded health system. METHODS: Thirteen key informant interviews at three public sector health organisations were conducted. Common themes were identified. An in-person workshop was held in Toronto on 15 May 2024. We identified barriers and enablers and make recommendations for advancing AI/ML in the broader public sector. RESULTS: A total of six barriers were identified by participants: 1) knowledge, education and expertise; 2) privacy, ethics and security; 3) technology and infrastructure; 4) financial barriers; 5) competing priorities; and 6) fear of being replaced. Key enablers include 1) partnerships; 2) buy-in and support from senior leadership; 3) management and operational supports; and 4) actionable use cases. One prominent use case objective was to enhance healthcare system efficiency, including automating routine or unskilled operations to allow healthcare professionals to focus on higher-value tasks. CONCLUSION: Demonstrating success from focused small-scale AI applications, partnering with academia, and engaging senior leadership may build confidence and capability, but it is important to share knowledge, experiences, and measure success for the perspectives of different partners.
Habbous et al. (Fri,) studied this question.