Industry · Healthcare
AI work for hospitals, primary care, pathology and the NDIS — with the clinical safety, AHPRA, TGA, and My Health Record realities accounted for.
AI in Australian healthcare runs into clinical-safety obligations that commercial AI work doesn't face. Some applications are TGA-regulated as Software as a Medical Device; many sit just under the threshold but still require clinical-governance scrutiny; almost all involve patient data that triggers Privacy Act and state health-records legislation. Our healthcare AI engagements are designed around those constraints — not the other way around. Clinically informed, AHPRA-aware, NSQHS-aligned.
Regulatory context
Every AI engagement we run in Healthcare produces documentation that explicitly maps the work to the obligations below. The risk register, the control framework, the board pack — they reference these by name, so internal audit and compliance teams can adopt the artefacts directly without translation.
Key challenges
AI tools that influence clinical decisions risk being classified as Software as a Medical Device. The boundary is consequential — being on the wrong side of it changes the regulatory pathway dramatically. We help map the use case against the TGA classification rules before architecture decisions are made.
AI deployments need to fit the existing clinical governance committees, incident-reporting processes, and outcome-monitoring systems. Bolting AI onto a hospital without that integration creates accountability gaps. The engagement plan includes clinical-governance touch-points from week one.
Data flows in healthcare cross multiple legislative frameworks. We map data residency, consent capture, and audit-trail requirements before the architecture is locked in.
Clinician adoption follows the same pattern as frontline rep adoption in FS, only more so: trust is earned slowly and lost instantly. We design the engagement around clinician input from day one, with explicit feedback loops the operator can see they're shaping.
Use cases
Ambient scribing, structured note generation, summary drafting. Reducing the documentation burden that drives clinician burnout — measurable in time-back-per-shift.
For primary care, telehealth, ED intake. Human-in-the-loop, with clear handoff thresholds and audit trails.
Augmentation, not replacement. SaMD-regulated where appropriate; designed for radiologist / pathologist workflow integration.
Plan management, participant communications, compliance documentation. Designed around NDIS Quality and Safeguards Commission obligations.
Services most relevant here
Practical AI governance for Australian businesses — policy, risk registers, board reporting, and the audit trail that satisfies both your CISO and your CEO.
Explore practice
From "we should do something with AI" to a prioritised, costed roadmap your team can actually deliver — with the people who would run it sitting in the workshop.
Explore practice
RAG, agents, evaluations and observability designed for the realities of running LLMs in production — cost, latency, accuracy and drift, all measured.
Explore practice
FAQ
For Class I SaMD (low risk), yes — we have engagement experience with the TGA classification and notification pathway. For Class II+ (higher risk), we partner with a specialist medical-device regulatory consultancy and lead the AI implementation alongside.
Yes — including the patient-consent, audit-trail, and security obligations the integration triggers. The engagement scope always includes a privacy impact assessment before architecture decisions are locked.
Both. Public health engagement patterns are closer to government work (procurement panels, lengthier review cycles); private engagement patterns are closer to commercial enterprise work. The methodology adapts.
Next step
We’ll come ready with questions specific to your industry and your regulator environment. 30 minutes, conversational, no commitment.