Primary Sense synthetic data

Start Date

7 January 2026

End Date

27 October 2026

Project Participants

WA Primary Health Alliance

Status: Completed

Tags: data analytics, data governance, data linkage, general practice, innovation, primary care, synthetic data

Opportunity 

Healthcare technology needs realistic data to support development, testing and reporting, but using real patient records introduces significant privacy, governance and access constraints.  

Primary Sense, developed by the WA Primary Health Alliance (WAPHA), is a comprehensive GP decision-support and data extraction tool designed to improve clinical insights and operational efficiency. As the platform continues to evolve, it is essential to validate a wide range of use cases under realistic conditions to ensure reliability and scalability. 

This project was an opportunity to explore whether synthetic data could provide a safer testing resource for Primary Sense while applying and testing the Synthetic Health Data Governance Framework in a real Australian primary-care setting. 

Project Objectives  

This project aimed to generate a usable synthetic dataset to support application development, performance testing and reporting use cases.

The project set out to apply the guidelines of the Synthetic Health Data Governance Framework (SHDGF) five-step process to ensure all outputs are privacy-preserving, high quality, and ethically compliant.  

It also engaged an independent third party to assess the implementation of the framework, and the quality of the resulting synthetic dataset. 

The project progressed through the first three SHDGF stages, covering use-case assessment, source-data assessment and preparation, and prototype synthetic-data generation. It produced a linked data design, two prototype synthesis approaches, privacy and utility evaluation tools, governance records, and architecture documentation. The independent review concluded that the design was well positioned for a future real data phase once any outstanding governance, configuration and engineering conditions were addressed. 

The project did not proceed to generate a synthetic dataset from real data, and as such did not complete the final evaluation to produce fidelity and utility evidence. 

The project established a technical foundation for generating a synthetic dataset from longitudinal primary care data while adhering to the SHDGF.  

It provides practical lessons for future application and validation of the SHDGF, in particular the importance of aligning data custodian processes, ethics decisions, social license, and technical release evidence before moving to production.

Integrity, Excellence,
Teamwork and Authenticity

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