What We Learned Deploying Clinical AI Inside a Public Health Record
Deploying an AI scribe inside a live public Digital Health Record is a different problem to building one — the technology is the easy part. Over the past period running MedTalk AI inside Canberra Health Services’ Digital Health Record, with over 200 registered clinicians now using it across 20+ specialties and 15+ regions, the lessons that mattered most weren’t about transcription accuracy. They were about trust, integration depth, and governance.
Why governance-first matters
Before MedTalk AI, our team led the Digital Delivery of COVIDSafe at the Australian Digital Transformation Agency (ADTA) — an experience that taught that a government-adjacent digital health tool lives or dies on trust, not features. Clinicians and health systems won't adopt a tool they can't audit, explain, or hold accountable, however accurate it is. That principle is why MedTalk AI was built around Epic and Best Practice Software certification from day one rather than as a bolt-on integration.
Lesson one: integration depth beats raw accuracy
Most AI scribes today produce reasonably accurate transcriptions. What separates a tool clinicians keep using from one they abandon after a week is whether output lands directly in the EHR they already use, in the expected format, without a manual copy-paste step.
Lesson two: adoption is a curve, not a switch
200 clinicians didn’t start using MedTalk AI on day one. Real-health-system adoption follows a curve — early adopters, then a larger group who need to see it work reliably for colleagues first, then holdouts who need the workflow built into onboarding.
Lesson three: the audit trail is a deployment requirement, not an afterthought
In a public health system, "can you tell us who accessed this note and when" is asked early and often. Building the audit trail and access logging into the architecture from the start is the difference between a pilot that scales and one that stalls in procurement.
Lesson four: documentation-time savings translate differently across specialties
The headline "up to 70% reduction in documentation time" figure isn’t uniform — it’s highest in specialties with long-form narrative documentation and lower in fast, templated consults. Reporting an honest range by specialty built more trust with clinical leads.
Where this is headed next
The next phase of work is less about the AI model and more about the governance frameworks health systems and boards will need to evaluate clinical AI responsibly as it moves from pilot to standard practice — a topic covered in our companion article, AI Governance in Healthcare.