Private AI Built for Australian Healthcare
Clinical decision support, medical record summarisation, and coding assistance — deployed on sovereign Australian infrastructure with HL7/FHIR integration and full patient data sovereignty.
Why Australian Healthcare Needs Private AI
Clinicians are drowning in documentation while patient data demands the highest level of protection. A custom LLM addresses both challenges within a sovereign, clinically validated framework.
Australian clinicians spend an average of 2.5 hours per day on clinical documentation — time diverted from direct patient care. Across Australia's 700 public hospitals and 630 private hospitals, this documentation burden translates to billions of dollars in clinical time consumed by administrative tasks. Meanwhile, clinical coding backlogs cost hospitals an estimated $180 per uncoded episode in delayed activity-based funding recognition.
Public AI tools are categorically unsuitable for healthcare. Patient data is protected under the Privacy Act 1988, the My Health Records Act 2012, and state-level health records legislation. The Australian Digital Health Agency's Clinical Governance Framework requires that clinical information systems maintain data sovereignty and support clinical audit. Sending patient data to US-hosted AI platforms violates these obligations and creates unacceptable clinical governance risks.
A custom LLM deployed on sovereign Australian infrastructure — integrated with your EMR via HL7 and FHIR — delivers AI-powered clinical decision support, documentation assistance, and coding automation without any patient data leaving your controlled environment. Every output is clinically validated, source-cited, and designed to augment rather than replace clinical judgement. The result is more time for patient care, faster revenue recognition, and better clinical documentation quality.
AI Capabilities for Healthcare
Every capability is designed for the specific clinical, regulatory, and governance requirements of Australian healthcare organisations.
Clinical Decision Support
Provide clinicians with evidence-based guidance at the point of care. The AI searches clinical guidelines, pharmaceutical references, and your hospital's approved protocols to surface relevant information during clinical workflows.
- Evidence-based clinical guideline retrieval at point of care
- Differential diagnosis support with cited clinical evidence
- Treatment pathway guidance aligned with hospital protocols
- Clinical alert generation for contraindications and interactions
Medical Record Summarisation
Generate concise, structured summaries from complex medical records including admission notes, progress notes, pathology results, and imaging reports. Reduces the time clinicians spend reviewing patient histories.
- Automated discharge summary generation from clinical notes
- Longitudinal patient history summarisation across encounters
- Structured handover document preparation for shift changes
- Specialist referral letter drafting from patient records
Clinical Coding Assistance
Accelerate clinical coding by suggesting ICD-10-AM, ACHI, and ACS codes from clinical documentation. The AI analyses discharge summaries and progress notes to recommend principal diagnosis, additional diagnoses, and procedure codes.
- ICD-10-AM code suggestion from discharge summary analysis
- ACHI procedure code recommendation with supporting evidence
- DRG assignment verification against coding standards
- Coding query generation for incomplete clinical documentation
Policy & Procedure Lookup
Instant natural language search across your hospital's clinical policies, administrative procedures, infection control protocols, and clinical practice guidelines. Staff find the right document in seconds.
- Natural language search across all clinical and admin policies
- Infection control protocol retrieval by pathogen or scenario
- Medication administration procedure lookup by drug and route
- Version-controlled retrieval ensuring only current policies returned
Drug Interaction Checking
Cross-reference medication orders against the patient's current medications, allergies, and clinical conditions. The AI checks interactions using the Australian Medicines Handbook, MIMS, and your hospital's formulary.
- Real-time drug-drug interaction analysis for medication orders
- Allergy and contraindication checking against patient records
- Dose range verification by indication, age, weight, and renal function
- Australian Medicines Handbook and MIMS cross-referencing
Clinical Trial Documentation
Support clinical research teams with protocol analysis, eligibility screening, adverse event reporting, and regulatory documentation. The AI understands NHMRC guidelines and TGA clinical trial requirements.
- Clinical trial protocol analysis and eligibility criteria matching
- Adverse event detection and reporting template generation
- NHMRC and TGA regulatory documentation assistance
- Literature review support for clinical research proposals
How Deployment Works for Healthcare
From clinical assessment to EMR integration, the process is designed for the governance and validation requirements of Australian healthcare.
Clinical Assessment
We assess your clinical systems, governance frameworks, and priority use cases with your clinical informaticists and department heads to design a deployment aligned with your care delivery model.
Knowledge Ingestion & Validation
Clinical guidelines, hospital policies, formulary data, and approved protocols are securely ingested. The model undergoes clinical validation against specialist benchmarks before deployment.
EMR Integration & Go-Live
We integrate the AI with your EMR via HL7/FHIR, deploy clinical decision support at the point of care, and conduct hands-on training for clinicians, coders, and administrative staff.
Related AI Solutions
Custom LLM for healthcare is part of a broader enterprise AI platform. Explore related solutions for health organisations.
On-Premises Deployment
For hospitals requiring the highest level of data control, deploy your custom LLM entirely within your own data centre.
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Explore insurance AI solutions →Frequently Asked Questions
Common questions from hospital CIOs, chief clinical information officers, and health network executives about deploying private AI.
The platform is classified as a clinical decision support tool, not a medical device, which means it falls outside TGA's mandatory registration requirements under the current regulatory framework. However, we design the system to align with TGA's emerging guidance on AI-based software. The AI never makes autonomous clinical decisions — it provides information retrieval and summarisation that clinicians use to inform their judgement. For AHPRA compliance, the system supports the Medical Board's position that AI tools must be used under practitioner supervision. All outputs include source citations and confidence indicators, and the platform maintains comprehensive audit trails that satisfy both TGA and AHPRA expectations.
Patient data is among the most sensitive information in any sector. Our healthcare deployments run entirely on sovereign Australian infrastructure — no patient data, clinical notes, or medical records are ever sent to offshore AI providers. The system complies with the Privacy Act 1988, the My Health Records Act 2012, and state-level health records legislation including the Health Records Act 2001 (Victoria) and Health Records and Information Privacy Act 2002 (NSW). All data is encrypted with AES-256 at rest and TLS 1.3 in transit, with role-based access controls aligned to your clinical governance framework.
Yes. Custom LLM supports both HL7 v2 messaging (which remains the dominant standard in Australian hospital systems) and FHIR R4 APIs. The system can ingest data from EMR systems via HL7 ADT, ORU, and ORM messages, and expose AI capabilities through FHIR-compliant APIs. For hospitals using Cerner, Epic, or local EMR platforms, we configure bidirectional integration during onboarding. The AI can read patient data from the EMR, provide clinical decision support, and write structured summaries back to the patient record via approved clinical workflows.
Clinical validation is critical for healthcare AI. Our clinical decision support capabilities have been validated against specialist clinician benchmarks across multiple domains. Medical record summarisation achieves 96.8% accuracy against specialist-generated summaries. Clinical coding suggestions achieve 94.2% accuracy on ICD-10-AM coding tasks. Drug interaction checking is cross-validated against the Australian Medicines Handbook and MIMS. We recommend that each deploying organisation conducts its own clinical validation using their specific patient population and clinical workflows, and we support this process with validation frameworks and statistical analysis.
The system can read My Health Record data when authorised through the existing clinical information system integration. It does not directly access the My Health Record infrastructure — instead, it leverages the data already available to clinicians through their EMR's My Health Record gateway. This approach complies with the My Health Records Act 2012 and the Healthcare Identifiers Act 2010. The AI can summarise relevant My Health Record data including discharge summaries, pathology results, and medications, presenting a consolidated view to support clinical decision-making.
Hospital deployments typically range from $8,000 to $25,000 per month depending on the number of beds, clinical departments integrated, and scope of functionality. A 200-bed hospital with clinical decision support, medical record summarisation, and clinical coding assistance typically sits at $12,000 to $15,000 per month. For context, clinical coding backlogs alone cost Australian hospitals an estimated $180 per uncoded episode in delayed revenue recognition. Most hospitals recover their AI investment within three months through coding efficiency gains and reduced clinical documentation time, which averages 2.5 hours per clinician per day.
Ready to Transform Your Health Organisation with AI?
Join forward-thinking Australian hospitals and health networks using private AI to reduce documentation burden, accelerate clinical coding, and support clinical decision-making — all with full patient data sovereignty.