The aim of precision oncology is to tailor cancer management not only to the tumour itself, but ultimately to the individual. Achieving equitable precision cancer care therefore requires health systems to accurately identify and understand population diversity.
Despite Australia's multicultural population, ethnicity is not routinely or comprehensively captured in healthcare datasets. Current approaches often rely on proxy measures such as country of birth or language spoken at home, which fail to reflect the complexity of ethnic identity, migration experiences and cultural diversity. As a result, our ability to identify inequities, understand population-specific needs and develop targeted interventions remains limited.
Australia's expanding data linkage capabilities provide new opportunities to address these gaps. Recently, the Australian Bureau of Statistics Person Level Integrated Data Asset (PLIDA), which includes Census and migration data, has been linked with cancer registries. This has enabled research involving previously "invisible" groups, including refugees and minoritised ethnic populations diagnosed with cancer. These linked data provide opportunity to generate important evidence on disparities in cancer outcomes, health service utilisation and access to care.
Emerging approaches are also leveraging unstructured data within oncology information systems to better understand molecular profiling in minoritised ethnic populations. In the setting of lung cancer, novel natural language processing methods can extract detailed ethnicity-related variables and molecular information, providing insights into variation in tumour biology, treatment patterns and outcomes.
Building on this work, an MRFF-funded program will evaluate a health data framework to improve the identification and engagement of minoritised ethnic populations, address fragmented data systems and support initiatives to provide more culturally responsive cancer care. Through co-designed implementation strategies and workforce initiatives, the program aims to translate better data capture into meaningful health-system improvements and more equitable precision cancer care.