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Gain advanced, practical expertise in AI governance and implementation for healthcare settings through this comprehensive professional course designed specifically for health and hospital managers. Develop the skills to evaluate organisational readiness for AI adoption using structured assessment frameworks, construct tiered governance structures including Board-level committee design, risk stratification, and AI tool inventories, and apply recognised leadership models to manage executive expectations and establish clear accountability for AI outcomes.
Master the analysis of AI tools using the Software as a Medical Device (SaMD) classification framework to determine regulatory obligations before procurement, and apply a six-pillar AI business case methodology covering clinical value, safety, financial return, workforce impact, equity, and regulatory compliance. Learn to identify critical contractual provisions in vendor agreements, including data rights, model drift monitoring, and exit clauses essential to protecting your organisation.
Explore evidence-based workforce retraining and role redesign strategies in response to AI-driven automation, and apply structured change management approaches to address each form of staff resistance — from safety concerns and job security fears to values conflicts and workflow disruption. Build an organisational AI culture grounded in psychological safety, critical adoption, and equity consciousness.
Navigate the complex international regulatory landscape by applying the deployer accountability framework across the EU AI Act, TGA SaMD obligations, FDA HIPAA and PCCP requirements, India's DPDPA, and ASEAN and African governance frameworks. Design jurisdiction-specific AI governance checklists that integrate procurement, privacy, human oversight, and incident response obligations into a coherent institutional approach.
Develop robust post-implementation review processes that assess clinical, financial, workforce, and equity outcomes of AI investments. Apply Total Cost of Ownership methodology to uncover hidden implementation, monitoring, and governance costs frequently omitted from vendor proposals, and recognise the six financial risk factors — including vendor lock-in, benefits realisation failure, and hidden equity costs — most commonly responsible for AI failing to deliver promised value.
Mandate equity impact assessments and disaggregated demographic performance data as conditions of AI procurement, identify the four types of algorithmic bias and their patient safety consequences, and design governance structures with community representation and digital inclusion infrastructure to prevent AI from amplifying existing health disparities across all six global regions.
Deploy a complete suite of AI governance instruments across three administrator levers — an AI governance procurement checklist, an AI tool inventory with lifecycle management, and a structured incident response protocol — each connected to named organisational roles with clear ownership, approval, use, and audit responsibilities for immediate operational implementation.
