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National Master Linkage Key

The challenge:

This project addresses the challenge of linking data across state, territory, and Australian Government systems, which currently hinders researchers from conducting accurate and comprehensive national and cross-border studies.

In Australia people and service-related data are linked by authorised Commonwealth, state, and territory data linkage units. While these units work well within their own areas, they face significant difficulties when linking data across state or territory borders or with Australian Government data.

Current systems were not designed for cross-jurisdictional use, which makes it hard for researchers to conduct studies that require national or cross-border data. This limits the ability to address important issues affecting people country-wide.

The approach:

Since 2016, the Population Health Research Network (PHRN) has funded the National Master Linkage Key (NMLK) project to create a reliable system for linking data across Australia. The project involves four phases:

Phase 1: Explored different models and identified what is needed for a national data linkage system.

Phase 2: Tested the NMLK to show how regular cross-border data linkage can work.

Phase 3: Improved the NMLK by addressing governance (rules for sharing data), developing ways to keep data up to date, and starting technical improvements.

Phase 4 (current): Helping data linkage units in states and territories prepare their systems to combine data from the Medicare Consumer Directory (MCD) with their existing records and linking these to the NMLK and the National Linkage Spine (NLS). Creating a national communications plan and resources to explain how the new system works to researchers, data providers, and other stakeholders.

Our collaborators:

Phase 4 of the NMLK Project involves the following partners:

  • The Australian Institute of Health and Welfare (Project Lead)
  • Centre for Victorian Data Linkage
  • Data Linkage Queensland
  • WA Department of Health
Status:

Ongoing

International Journal of Population Data Science, 2024 ‘A NSW example’