Overview

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This advanced course develops practical AI governance for health organisations, covering readiness assessment, board accountability, regulatory obligations, procurement, workforce change, equity safeguards, and post-implementation review. Learners design governance checklists, tool inventories, incident protocols, and accountability structures for operational use.

Syllabus

Evaluate a health organisation's readiness for AI adoption using structured assessment frameworks, construct a tiered AI governance framework including Board-level committee structure, risk stratification, and AI tool inventory, and apply recognised leadership models to manage board expectations, recruit clinical champions, and establish executive accountability for AI outcomes.
Analyse AI tools using the SaMD classification framework to determine regulatory obligations before procurement, apply the six-pillar AI business case methodology to evaluate clinical value, safety, financial return, workforce impact, equity, and regulatory compliance, and identify the contractual provisions — including data rights, model drift monitoring, and exit clauses required in every AI vendor agreement.
Implement evidence-based workforce retraining and role redesign strategies in response to AI-driven automation, apply structured change management responses to each form of staff AI resistance — safety concerns, job security fears, values resistance, and workflow disruption and build an organisational AI culture characterised by psychological safety, critical adoption, and equity consciousness.
Apply the deployer accountability framework under the EU AI Act, TGA SaMD obligations, FDA HIPAA and PCCP requirements, India DPDPA, and ASEAN/African governance frameworks to institutional AI implementation decisions, and design a jurisdiction-specific AI governance checklist integrating procurement, privacy, human oversight, and incident response obligations.
Design a post-implementation review process that assesses clinical, financial, workforce, and equity outcomes of AI investments, apply Total Cost of Ownership methodology to identify 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 — historical, measurement, label, and deployment context mismatch and their patient safety consequences, and design governance structures with community representation and digital inclusion infrastructure to prevent AI amplifying existing health disparities across all six global regions.
Implement a complete suite of AI governance instruments across three administrator levers — AI governance procurement checklist, AI tool inventory with lifecycle management, and a structured incident response protocol: connecting each instrument to named organisational roles with clear ownership, approval, use, and audit responsibilities for immediate operational deployment.