Clinical AI in Canadian Community Hospitals: A Framework for Implementation Readiness

Clinical Artificial Intelligence (AI) tools are frequently developed in academic centres yet deployed in community hospitals with different workflows, staffing, and oversight. In Canada, where community hospitals constitute approximately 90% of hospitals, this creates a validation-to-implementation gap. Health Canada's 2026 pre-market guidance for machine learning-enabled medical devices addresses manufacturer responsibilities but leaves hospital implementation responsibilities under-specified. This article proposes a framework for community hospital AI implementation readiness organized around four dimensions (contextual validity, sociotechnical workflow integration, calibrated human-AI reliance, and adaptive safety, equity, and governance), operationalized through simulation-based systems testing. The framework specifies what a community hospital must build to deploy a specific AI tool safely, extending an established Canadian tradition of simulation-based systems testing to the lifecycle risks of AI. A real Canadian deployment, the CHARTwatch early warning system, illustrates the dimensions in practice. Safe AI deployment in community hospitals is an institutional capacity, not a procurement decision. Leaders should treat implementation readiness as core infrastructure investment.