From care record to research dataset
Your care data becomes research-ready without a new data project every time. Clinical data is aligned to FHIR R4 / AU Core and coded to SNOMED CT-AU, so you can build cohorts, de-identify and export without a separate data warehouse.
Community care intervention research.
Quality Indicator data and resident outcome studies.
NDIS outcome measurement studies.
Referral pathway and discharge analysis.
Research-ready by design
Cohorts from operational data
Most research platforms need a separate data warehouse, a separate ETL pipeline and a separate coding exercise. HealthOS captures structured, coded clinical data during normal care delivery. The record that drives service orders and progress notes is the record you query for research.
- Query by diagnosis code
- Filter by assessment scores and clinical observations
- Segment by service type, funding stream or location
- Combine criteria across the full clinical record
Structured from the start
Every clinical entry is captured as structured data against Australian terminology standards at the point of care. Nothing has to be cleaned up for research after the fact. The Common Data Model browser lets you query the underlying CDM directly, so you can understand the shape of the data, inspect value distributions and check coding completeness before you build a cohort.
De-identification you can trust
Export research-ready datasets with identifiers removed. The engine removes direct identifiers such as names, Medicare numbers and addresses, hashes the MRN and generalises dates of birth to age bands. Every export carries a de-identification audit trail an ethics committee can inspect.
- Direct-identifier removal
- MRN hashing
- Dates of birth generalised to age bands
- An audit trail for every export
From care setting to publication
Clinicians and researchers in the same organisation can move from a clinical question to a structured dataset without waiting for a data team to build a pipeline. Cohorts export as FHIR R4 bundles conformant with the AU Core profiles, or as CSV or JSON, which slot straight into research tools and statistical packages.
- FHIR R4, CSV and JSON export formats
- Cohort definitions saved and reproducible
- Ethics-ready extracts with a de-identification audit trail
- Seven years of longitudinal data from the audit trail
AI on your terms
HealthOS de-identifies clinical records and sends them to a large language model running in your own cloud account. You can generate clinical handover summaries, discharge letters and research insights, and patient data never leaves your sovereign boundary.
- De-identified before it reaches the LLM
- Runs in your own cloud account, with no shared API
- Clinical handover, discharge summaries and shift reports
- A full audit trail of every AI interaction
Trial-grade infrastructure
The research module goes beyond cohort extraction. It supports the full study lifecycle, from protocol design through participant enrolment, randomisation, scheduled assessments and compliant data export, on the same platform clinicians use every day. You can run interventional studies without standing up a separate EDC or CTMS.
- Randomised allocation with configurable arms and ratios, with every allocation recorded
- Protocol-defined visit schedules with windows and tolerances
- Progress tracked against the protocol timeline, with overdue assessments flagged
- Informed consent tracked by study, version and participant, including withdrawal, re-consent for protocol amendments and a complete consent audit trail
See it in action
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See the research platform in action
We'll walk you through it on real data. Bring your questions.