Oct 5, 2025
6
min read
Medically Reviewed
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The Pillars of AI Governance
Good AI governance in healthcare rests on four pillars. The first is transparency: patients and clinicians should know when they are interacting with AI, what the AI is doing, and how its outputs are generated. This does not mean that every user needs a technical explanation of the underlying model, but it does mean that the role of AI in each interaction should be clearly communicated and that the AI should not be designed to deceive users about its nature.
The second pillar is accountability: there must be a clear line of responsibility for the AI’s outputs. In healthcare, accountability ultimately rests with the clinician or practice that is using the AI, not with the AI itself or its vendor. The clinician reviews and signs the AI-generated clinical note, taking responsibility for its accuracy and completeness. The practice manager reviews and submits the AI-checked billing claim, owning the compliance decision. The AI is a tool that augments human judgement; it does not replace it, and the human remains responsible for the final decision in every case. Organisations that are clear about accountability structures before deployment find that staff are more confident using AI outputs, because the line of responsibility is explicit rather than assumed.
The third pillar is monitoring: the performance of AI systems must be tracked continuously to ensure they are functioning as intended and not degrading over time. An AI receptionist that handled bookings well when first deployed may become less accurate as patient demographics or practice schedules change. An AI scribe that performed well on one clinician’s patient population may not generalise to another. Monitoring systems detect these shifts early and trigger corrective action before patients or operations are affected, providing the ongoing assurance that the AI continues to perform to the standards that were validated at deployment.
The Fourth Pillar: Safety and Compliance
The fourth pillar of AI governance is safety and compliance. The AI platform must meet the regulatory and security standards that apply to healthcare technology in Australia. This includes compliance with the Privacy Act, the Notifiable Data Breaches scheme and any standards set by the Australian Digital Health Agency. It includes the technical security measures — encryption, access controls, audit logging — that protect patient data from breach or misuse.
For AI specifically, safety includes the requirement that the AI be triggered-only in its listening. This means the system does not record or process audio until a consultation or call explicitly begins, and it stops processing when the interaction ends. The patient’s conversations are not monitored, analysed or stored outside the specific context of the care they are receiving. For practices that serve vulnerable populations — aged care residents, mental health patients, children — this safeguard is particularly important. Practices should request written confirmation from any AI vendor that their system operates on a triggered-only basis and that no audio is retained or processed after the interaction has concluded.
Compliance also extends to the AI’s clinical safety in real practice settings. An AI that generates clinical notes or suggests billing codes must have its accuracy validated against relevant Australian standards and must include clear mechanisms for clinicians to flag and correct errors when they occur. The vendor should be able to demonstrate that its AI has been tested on Australian clinical data and Australian practice workflows, not only on general-purpose data sets that may not reflect the local context or regulatory environment. Governance is not a checkbox exercise; it is an ongoing commitment to safety that must be embedded in the design of the AI platform from the beginning.
Expert Tips
"Governance is not the enemy of innovation; it is the structure that allows innovation to happen safely. In healthcare, where the consequences of failure affect real patients, governance is what separates a responsible experiment from a dangerous gamble. The practices that embrace AI governance — that ask hard questions about transparency, accountability and monitoring before they deploy — are the ones that will be able to adopt AI confidently and scale it sustainably. The practices that skip governance to move faster will eventually be forced to stop and rebuild." — Arash Zohuri, CEO, MediQo
Governance in Practice: What It Looks Like Day to Day
At the practice level, good AI governance translates into concrete daily practices. It means that when a new AI tool is introduced, the practice holds a brief training session that covers not only how to use the tool but also what it does, what its limitations are, and how to report concerns. It means that the practice maintains a simple log of any errors or unexpected behaviours the AI exhibits, and that this log is reviewed periodically to identify patterns that may need attention.
It means that the practice has a clear policy about patient disclosure. When a patient calls and speaks with an AI receptionist, the system should identify itself as an AI. When AI-generated clinical notes are used, the patient should be informed as part of the consent process. These disclosures are not only ethically important; they also build trust by demonstrating that the practice is transparent about its use of technology. Patients who understand that AI is being used, and who see that its use is governed by clear policies and human oversight, consistently report higher confidence in the quality and safety of their care.
It means that the practice has designated a person — typically the practice manager or a senior clinician — who is responsible for the AI governance function. This person does not need to be a technical expert, but they need to know what AI tools the practice is using, what they are designed to do, and who to contact at the vendor if issues arise. In a small practice, this responsibility may take only a few hours per month, but having a designated accountable person makes a significant difference to the rigour of governance and ensures that AI oversight does not fall through the cracks of competing daily priorities. That person also serves as the point of contact for staff who have questions or concerns about the AI, creating a clear channel for feedback that might otherwise go unvoiced or be lost in the busy day-to-day operations of the practice.
Key Takeaways
AI governance ensures that AI tools are used safely, ethically and in compliance with regulatory requirements.
Good governance includes transparency about how AI works, clear accountability for its outputs and mechanisms for monitoring its performance.
Australian healthcare organisations should insist on AI platforms that demonstrate robust governance frameworks before deployment.
Governance is not a barrier to AI adoption; it is the foundation that makes responsible AI adoption possible.
As artificial intelligence becomes more embedded in healthcare operations, the question of governance has moved from a theoretical concern to a practical necessity. Governance in this context means the frameworks, policies and practices that ensure AI is used safely, ethically and in compliance with relevant regulations. It covers how AI tools are evaluated before deployment, how their performance is monitored over time, how accountability for their outputs is assigned, and how patients are informed about their use.
The need for governance is not unique to AI; healthcare has long had governance frameworks for clinical decision-making, data management and quality assurance. AI introduces new dimensions to these existing frameworks because it operates differently from traditional software. AI systems learn from data, their behaviour can change over time as they are exposed to new information, and their decision-making processes are not always transparent to human observers. These characteristics require governance approaches that are adapted to the specific risks and opportunities of AI.
This article explains what good AI governance looks like in the context of Australian general practice, aged care and allied health. It provides a practical framework that practice owners and managers can use to evaluate their own AI governance practices and to assess whether their AI vendors are meeting the standards that responsible healthcare demands.
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