Queensland Health Artificial Intelligence Framework
Queensland Health's statewide framework for artificial intelligence: governance requirements under ISO/IEC 38507 and a structured AI risk assessment, scaled governance between Hospital and Health Services and the Department, clinical governance and TGA requirements, consumer representation, defined roles and an AI Oversight Committee, guiding principles built on the quintuple aim and Australia's AI ethics principles, and a six-stage gated lifecycle from problem identification and risk assessment through delivery requirements, business case and validation, deployment approval, staged deployment with training and registry, to ongoing monitoring of KPIs, drift and fairness, retraining and controlled discontinuation.
Queensland Health Artificial Intelligence Framework is a compliance framework from Australia, Queensland (Queensland Health and its Hospital and Health Services) with 3 domains and 9 controls. The largest domains are The six-stage AI project lifecycle and its gates – Queensland Health Artificial Intelligence Framework (6 controls), Governance: policy requirements, HHS and departmental governance, clinical and ICT governance, consumers, roles and oversight – Queensland Health Artificial Intelligence Framework (2 controls), Guiding principles: the quintuple aim and Australia's AI ethics principles – Queensland Health Artificial Intelligence Framework (1 controls). Every control below carries what it requires and what an assessor expects to see.
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Framework Domains (3)
Governance: policy requirements, HHS and departmental governance, clinical and ICT governance, consumers, roles and oversight – Queensland Health Artificial Intelligence Framework
| Code | Title |
|---|---|
| queensland-health-artificial-intelligence-framework::G1 | Governance policy requirements: use a consistent, evidence-based process to evaluate the transparency, accountability and risk of procuring, developing, using or discontinuing AI, applying a structured AI risk assessment that considers all relevant factors and satisfies ICT profiling reporting; and establish AI governance based on the current ISO/IEC 38507, integrated into existing ICT, risk and information security management governance so that AI use is deliberate, effective and managed |
| queensland-health-artificial-intelligence-framework::G2 | Where governance sits and who holds the roles: low or medium risk solutions used in a single Hospital and Health Service within its risk appetite may be governed there, while high risk or potentially statewide projects need eHealth and departmental advice; clinical AI follows rigorous clinical governance pairing technical and clinical expertise plus Therapeutic Goods Administration requirements; consumer representation is considered across the lifecycle and in governance, design and testing; and human oversight is secured through the AI initiative owner, coordinating team, executive sponsor, decision maker with financial delegation, system owner and a statewide AI Oversight Committee that refines the Framework, develops the risk tool, enforces the principles, assesses risks, approves and monitors projects and ensures legal compliance |
Guiding principles: the quintuple aim and Australia's AI ethics principles – Queensland Health Artificial Intelligence Framework
| Code | Title |
|---|---|
| queensland-health-artificial-intelligence-framework::P1 | Guiding principles: prioritise and design AI to serve the quintuple aim, improving patient outcomes through safe, effective solutions that reduce human bias, using resources efficiently with demonstrated value for money and no duplication, supporting workforce wellbeing by automating repetitive and augmenting complex tasks and personalising training, improving consumer experience through digital tools, and driving equity by designing for marginalised and regional and remote communities from the start and validating algorithms locally; and apply Australia's eight AI ethics principles (human, societal and environmental wellbeing, human-centred values, fairness, privacy protection and security, reliability and safety, transparency and explainability, contestability, accountability) in developing and using AI |
The six-stage AI project lifecycle and its gates – Queensland Health Artificial Intelligence Framework
| Code | Title |
|---|---|
| queensland-health-artificial-intelligence-framework::S1 | Stage 1, problem identification, risk considerations and concept development (Gate 0): define the problem and SMART benefits, check the AI project repository for existing solutions, confirm strategic alignment, run an environmental and horizon scan, complete the AI Risk Assessment Tool including alignment with the guiding principles, identify options (in-house, partnership, procurement and do-nothing, standalone or statewide) with their resources, users and human-in-the-loop model, define high-level infrastructure and data needs and technical feasibility, identify stakeholders, governance including clinical governance, and regulation such as TGA software as a medical device rules, secure endorser support, and obtain the decision maker's written decision using a transparent prioritisation matrix |
| queensland-health-artificial-intelligence-framework::S2 | Stage 2, project delivery requirements (Gate 1): define business objectives, establish project governance and plan proportionate to risk, complexity and cost with clinical governance for clinical workflows and consumer representation, confirm information security provisions can be met, update the risk assessment and categorise risks with management plans, consult stakeholders including consumers and community, obtain legal advice on confidentiality, data access, legislation, intellectual property, auditability of AI decisions, cyber security, TGA, research governance, privacy and cloud or offshoring, evaluate options in detail for value for money with costs, infrastructure and enterprise architecture alignment, data governance and privacy impact assessment, workforce and training needs, follow the procurement framework if going to market, and secure research governance and ethics approval for research partnerships, leading to a written decision on build, partner or procure |
| queensland-health-artificial-intelligence-framework::S3 | Stage 3, business case (Gate 2): fully develop the preferred option with reconfirmed governance and team, a risk management plan for all risks to date, financial controls and funding, stakeholder analysis, regulatory and legal approval pathways such as TGA, model validation criteria and integration capacity; for in-house or partnership builds assess skills, authorise platforms and data sharing, define and split datasets, design architecture, co-design with end users, train with interpretability and bias mitigation, validate on unseen data, assess impacts and report with a model card; for procurement establish commercial expertise, approve the procurement strategy, evaluate vendors on healthcare AI experience, privacy and security, interpretability and bias mitigation, support and due diligence, and set evaluation criteria; then evaluate performance against predefined thresholds covering performance, interpretability, fairness, safety, control, security, privacy, robustness and reproducibility and across diverse consumers, establish total cost of ownership, document datasets and parameters, draft deployment, validation, retraining and continuity specifications, load testing and integration design, update the risk assessment and consult change management plans |
| queensland-health-artificial-intelligence-framework::S4 | Stage 4, solution assessment or contract award (Gate 3): develop the implementation plan addressing outstanding risks, budget and timelines; obtain approval to deploy on a performance report using unseen data or validated pilot results; for procurement assess suppliers against objectives and benefits with vendor information on accuracy and transparency, award the contract and service level agreements and obtain legal and contractual advice; conduct social and environmental impact assessment, resolve stakeholder concerns, complete ICT project assurance, set minimum quality standards, assess vendor and technology reputational risk and output auditing needs, update and communicate change management, agree minimum service availability (accessibility, uptime, response time, downtime procedures), test the technical integration against workflows and data pipelines and the user interface with stakeholders, approve the risk management plan and update the risk assessment, and set KPI minimum standards at warning and critical levels; clinical applications also pass clinical governance |
| queensland-health-artificial-intelligence-framework::S5 | Stage 5, deployment and integration (Gate 4): confirm implementation, integration, rollout and operation plans are approved and current, communicate risk management plans with controls in place, complete user and system testing satisfactorily, prepare training resources and schedules, establish and communicate technical and other support, deliver project assurance and status reporting to governance, update the risk assessment; have system owners deploy and monitor the solution, give production data access to beta testers only, keep version control of source code and models for audit on demand and update the AI solution registry; implement change management with documented new or changed roles meeting HR and industrial relations requirements, train users before deployment, pilot with early adopters, report beta results for approval and roll out in stages assessing impact between them, using a silent trial for complex or clinical use cases, until all impacted staff have access |
| queensland-health-artificial-intelligence-framework::S6 | Stage 6, ongoing monitoring and evaluation (Gate 5): establish KPIs aligned with the guiding principles covering quality (such as precision, recall and number needed to test), availability, responsiveness, data quality, drift and outliers, and fairness, inclusion and non-discrimination for every solution, with minimum standards at warning and critical levels; define retraining triggers, implement alerts to the system owner when standards are breached, monitor proactively for degradation, drift and unintended consequences with reports to governance or the oversight committee, build user feedback and audit mechanisms, re-evaluate regularly against the Stage 1 and 2 benefits and report periodically; retrain or refine as required and update the AI solution registry; report adverse events for registered software as a medical device to the TGA; and define and obtain approval for discontinuing the solution without affecting service delivery, recording the cessation date in the registry |
What is Queensland Health Artificial Intelligence Framework and who does it apply to?
Queensland Health Artificial Intelligence Framework is a compliance framework from Australia, Queensland (Queensland Health and its Hospital and Health Services) with 3 domains and 9 controls. Queensland Health's statewide framework for artificial intelligence: governance requirements under ISO/IEC 38507 and a structured AI risk assessment, scaled governance between Hospital and Health Services and the Department, clinical governance and TGA requirements, consumer representation, defined roles and an AI Oversight Committee, guiding principles built on the quintuple aim and Australia's AI ethics principles, and a six-stage gated lifecycle from problem identification and risk assessment through delivery requirements, business case and validation, deployment approval, staged deployment with training and registry, to ongoing monitoring of KPIs, drift and fairness, retraining and controlled discontinuation. It is used by organisations to establish and maintain compliance with industry standards and regulatory requirements.
What does Queensland Health Artificial Intelligence Framework actually require?
Queensland Health Artificial Intelligence Framework has 9 controls organised across 3 domains. The largest domains are The six-stage AI project lifecycle and its gates – Queensland Health Artificial Intelligence Framework (6 controls), Governance: policy requirements, HHS and departmental governance, clinical and ICT governance, consumers, roles and oversight – Queensland Health Artificial Intelligence Framework (2 controls), Guiding principles: the quintuple aim and Australia's AI ethics principles – Queensland Health Artificial Intelligence Framework (1 controls). Each control defines specific requirements that organisations must implement to achieve compliance.
If I already comply with another framework, how much of Queensland Health Artificial Intelligence Framework do I already cover?
Queensland Health Artificial Intelligence Framework does not currently have cross-framework mappings in our system. Check back as we continuously expand our mapping database.
How do I implement Queensland Health Artificial Intelligence Framework?
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