FHIR R4 / AU Core aligned clinical data, coded to SNOMED CT-AU and ICD-10-AM. Build cohorts, de-identify and export — without a data warehouse.
Book a demoQuery the clinical record by diagnosis, age, service type, assessment score or any coded field. Build research cohorts from real operational data.
Strip personally identifiable information from datasets before export. Names, dates of birth, addresses and identifiers removed or generalised automatically.
Export cohorts as FHIR R4 bundles conformant with AU Core profiles, CSV or JSON. Standard formats that slot straight into research tools and statistical packages.
Every clinical entry is coded to SNOMED CT-AU, ICD-10-AM and ACHI. Structured data from the point of care — not retro-coded for research after the fact.
De-identified clinical records sent to your own cloud LLM. Generate handover summaries, discharge letters and clinical insights — data never leaves your sovereign boundary.
Browse and query the underlying CDM directly. Understand the data shape, inspect value distributions and verify coding completeness before building cohorts.
Block randomization setup for intervention studies. Configurable arm allocation, stratification by site or characteristic, and sealed envelope reveal — all audited.
Define study visit schedules with windows and tolerances. HealthOS tracks participant progress against the protocol timeline and flags overdue assessments.
Track informed consent by study, version and participant. Manage withdrawal, re-consent for protocol amendments and maintain a complete consent audit trail.
Query by diagnosis, assessment score, service type or any coded field. De-identify and export as FHIR R4, CSV or JSON — without a separate data warehouse.
Most research platforms require a separate data warehouse, a separate ETL pipeline and a separate coding exercise. HealthOS captures structured, coded clinical data during normal care delivery. The same record that drives service orders and progress notes is the record you query for research.
Export research-ready datasets with personally identifiable information removed. The de-identification engine handles names, dates of birth, Medicare numbers, addresses and free-text identifiers — giving researchers access to rich clinical data without compromising privacy.
HealthOS bridges the gap between operational care data and academic research. Clinicians and researchers working within the same organisation can move from a clinical question to a structured dataset without waiting for a data team to build a pipeline.
HealthOS de-identifies clinical records and sends them to a large language model running in your own cloud account. Generate clinical handover summaries, discharge letters and research insights — without patient data ever leaving your sovereign boundary.
HealthOS goes beyond cohort extraction. The research module supports full study lifecycle management — from protocol design through participant enrollment, randomization, scheduled assessments and compliant data export. All within the same platform clinicians use daily.
A 45-minute walkthrough with real data from a reference deployment.