Healthcare Data Governance in the AI Era: What's Actually Different

I’ve had the same conversation three times this month. A healthcare executive asks: “We have data governance in place. Isn’t that enough for AI?”

The short answer is no. The longer answer is more interesting.

Traditional healthcare data governance focuses on access control, privacy, and compliance. Important things. But AI creates new governance requirements that most existing frameworks don’t address.

Let me explain what’s actually different.

Training Data Governance

When you deploy a commercial AI system, it was trained on data from somewhere. That data shapes everything the AI does—its capabilities, its biases, its failure modes.

Most healthcare organisations evaluate AI systems based on performance claims. Does it detect findings accurately? Is it fast enough? Does it integrate with our systems?

Fewer ask hard questions about training data:

  • Where did the training data come from?
  • How similar is it to our patient population?
  • Were appropriate consents obtained for AI training use?
  • How was the data labelled, and by whom?

This matters because training data problems propagate into clinical use. An AI trained primarily on one demographic performs worse on others. An AI trained on data from academic medical centres may not generalise to community hospitals.

Your data governance framework should include criteria for evaluating training data provenance. Not just for AI you’re buying, but especially for AI you might build using your own clinical data.

Model Performance Governance

Traditional data governance asks: Is the data accurate? Is it complete? Is it properly secured?

AI adds a new question: Is the model performing as expected?

Model performance isn’t static. Even “locked” algorithms (ones that don’t learn from new data) can experience performance drift. Patient populations change. Clinical practices evolve. Disease prevalence shifts. An AI that performed well two years ago might not perform as well today.

Governance frameworks for AI need to include:

Baseline performance documentation. What was the model supposed to do? What performance levels were expected?

Ongoing performance monitoring. How do you know the model is still performing as expected? What metrics are you tracking, and how often?

Drift detection. When performance degrades, how do you detect it? Most organisations wait for obvious problems. By then, harm may have occurred.

Remediation protocols. When performance issues emerge, what happens? Who decides whether to continue using the system while investigating?

This is fundamentally different from traditional data governance. You’re not just governing data—you’re governing a system that makes inferences from data.

Inference Data Governance

Every time an AI system processes a clinical case, it creates new data: the inference itself. The AI’s assessment. Its confidence level. Whether a clinician agreed or disagreed. The eventual outcome.

This inference data has significant value:

  • It can be used to monitor model performance
  • It can identify cases for quality review
  • It can support research on AI effectiveness
  • It can train future models (with appropriate consent)

But it also raises governance questions:

Who owns inference data? The vendor? The healthcare organisation? The patient?

How is inference data protected? It’s derived from patient data, so patient privacy applies. But inference data might also reveal proprietary information about AI behaviour.

Can inference data be shared? If multiple organisations deploy the same AI, can their inference data be aggregated to improve the model? Under what consent and governance frameworks?

Most vendor contracts are silent on these questions, or heavily favour the vendor. Negotiate proactively.

Algorithm Transparency Governance

AI algorithms are often opaque. Not deliberately—neural networks genuinely work in ways that resist simple explanation. But opacity creates governance challenges.

You can’t govern what you can’t see.

Transparency requirements for AI governance:

Documentation of intended behaviour. What is the AI supposed to do? What are its limitations? Where might it fail?

Explainability requirements. Can the AI explain individual decisions? (Not all AI can.) If not, how do clinicians understand why a particular recommendation was made?

Access to performance data. Does the vendor share detailed performance analytics? Or just aggregate statistics that might hide important variations?

Update transparency. When the vendor updates the algorithm, what changes? How are you notified? Do you have the right to refuse updates?

Traditional consent frameworks weren’t designed for AI. They assumed data would be used for direct care, or for specified research purposes.

AI introduces new complexities:

Secondary use for training. Can clinical data be used to train AI models? Under what consent framework? Does the patient need to specifically consent to AI training, or is it covered by general consent to data use?

Commercial implications. If patient data trains a commercial AI system that generates revenue, does the patient have any claim? Australian law doesn’t address this clearly.

Consent for AI-assisted care. Should patients know when AI is involved in their care? Currently, they usually don’t. Is that acceptable?

Opt-out mechanisms. If patients don’t want AI involved in their care, how do they express that? Can they opt out of AI-assisted diagnosis while still receiving care?

The ADHA has started developing guidance on consent for digital health, including AI. But implementation is up to individual organisations. You need policies and processes.

Building an AI Data Governance Framework

If you’re starting from scratch (or more likely, augmenting an existing framework), here are the key elements:

AI register. What AI systems are in use across your organisation? (Many organisations can’t answer this quickly.)

Training data requirements. Criteria for evaluating training data provenance for any AI you deploy or develop.

Performance monitoring standards. How will you monitor AI performance, and who is responsible?

Inference data policies. Clear rules about how inference data is stored, used, and shared.

Transparency requirements. Minimum transparency expectations for AI vendors.

Consent framework. Policies on patient consent for AI in care and AI development.

Governance committee. Who oversees AI data governance? This probably shouldn’t be your traditional data governance committee without augmented expertise.

This Is Hard

I won’t pretend this is simple. AI data governance requires expertise that most healthcare organisations don’t have internally. It requires investment in monitoring infrastructure. It requires negotiating with vendors who may resist transparency requirements.

For organisations tackling this seriously, working with external partners who have AI governance expertise can help. AI consultants Sydney and specialised health informatics consultancies increasingly offer governance support alongside implementation services.

But ultimately, governance is your responsibility. You can’t outsource accountability. External expertise helps you build capability; it doesn’t replace the need to develop your own.

The organisations that get AI data governance right will have a significant advantage. Not just in deploying AI safely, but in building the trust—with patients, clinicians, and regulators—that sustainable AI adoption requires.

Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.