Data demonstration projects
The Australian Institute of Health and Welfare (AIHW) is undertaking data demonstration projects to inform development of a National Primary Health Care Data Collection (NPHCDC).
Initially focusing on general practice data, AIHW is partnering with Primary Health Networks (PHNs) and extractor organisations across different projects to explore and understand data collection, quality, consistency, transformations, storage and release methods, including the analytical potential of the data.
These proof-of-concept projects are designed to inform the requirements for an NPHCDC and generate valuable insights into the health of Australians, which are otherwise not available.
Demonstration projects aim to:
- enhance knowledge around general practice activity and patterns of care delivery in primary care
- expand understanding of health conditions that are a priority for Australia
- address primary health care information gaps to inform health service delivery and patient outcomes
- lead to insights and outcomes that can be adapted and applied to a range of data
- develop a deeper understanding of data quality
- enable data availability through robust sharing arrangements and interoperability, by promoting consistent and robust data governance around standards and classification.
If you would like more information on current data demonstration projects or to discuss a project opportunity, contact [email protected].
AIHW and 17 PHNs partnered on a data demonstration project across 2023-24, to understand dementia diagnosis in general practice using aggregate general practice data. The project used Primary Health Insights (PHI), the secure platform used by most PHNs for storage and analytics of their general practice data.
This project:
- tested how aggregate general practice data from PHNs can enable insights into a specified health condition
- examined variations in data governance, supply, transformation, and analytics.
The project demonstrated that general practice data has the potential to provide insights into the health conditions that impact the Australian population. It also highlighted the value of collaboration between the AIHW and PHNs in establishing an NPHCDC.
Key findings, challenges and opportunities from the project have been published in the report: Towards a national primary health care data collection – dementia demonstration project.
A data demonstration project began in 2025 with the Western Australia Primary Health Alliance (WAPHA) to analyse primary care and health outcomes of people diagnosed with mental illness. The project is using de-identified unit record data from Western Australia general practices through the Primary Health Insights (PHI) platform.
Continuing through to mid-2026, this project will:
- lead to novel insights on care and outcomes for people diagnosed with mental illness
- support PHN needs assessments
- deliver better outcomes under the Equally Well initiative
- build analyses that can be applied across PHNs.
The AIHW is working with participating PHNs from Western Australia, most of Victoria and some of New South Wales to explore the breadth and potential of de-identified general practice data using Primary Sense and POLAR. These projects are designed to provide prevalence estimates for important health conditions to inform estimates of burden of disease and health expenditure. The methodology of this project can be adapted across PHNs to better understand health conditions and service planning in their community.
The AIHW is collaborating with participating PHNs and Outcome Health across most of Victoria and some New South Wales areas on several projects. These projects seek to understand the potential primary care data has in closing health data gaps and enhancing quality improvement by investigating:
- the viability of converting free text data into classified privacy-preserved health data by testing the automated coding of ‘reason for visit’ data from free text content to both SNOMED CT and International Classification of Diseases, 11th revision (ICD-11) codes using large language models
- how to enhance the quality, validity and application of general practice data for quality improvement purposes by accessing more granular data via a curated datamart from the Practice Incentives Program Quality Improvement (PIPQI) measures
- the usefulness of general practice data for closing key sexual and reproductive health data gaps.