Ms Alexandra Shine1,2
1Flinders University, Alice Springs, Australia, 2CQUniversity, Rockhampton, Australia
Biography:
With a background in critical care and primary health nursing, working as a remote generalist all around Australia including assisting with overseas disaster relief, Alexandra is now a novice researcher and academic – currently undertaking a PhD student with CQUniversity, and is a Lecturer in Remote Health Practice with Flinders University, NT. In between reading, writing, drinking copious amounts of coffee, and hanging out with her dogs, Alexandra is questioning artificial intelligence integration and its impact on patient advocacy and clinical nursing practice, with a focus on nursing ethics in a future of algorithm-driven healthcare.
Abstract:
Data driven technologies and health informatics promises major gains in healthcare by improving diagnostics, treatment recommendations, and population health outcomes. Yet these benefits are unevenly distributed. In rural and remote areas—often shaped by colonial legacies, structural marginalisation, and persistent “data deserts”—health data is often incomplete, inconsistently collected, or at risk of being absent altogether. These gaps not only undermine the process of clinical decision making, but risk embedding historical inequities into contemporary AI-integrated systems, perpetuating health inequities. As nursing practice increasingly intersects with algorithmic based tools, understanding how these biases form from data marginalisation is vital for nurses to provide ethical, equitable care.
Health data is what is collected and used to inform health policy and evidence-based practices; however in computational sciences, these health datasets are frequently aggregated into AI-systems without full transparency, consistent quality, or culturally safe consent processes.
Competent practice requires recognising where “evidence based” may mean “statistically convenient.” Rural and Remote Nurses must navigate the ethical tensions of using data driven tools that are not designed to reflect local realities. Issues of data quality, informed consent for secondary use, and cross institutional data security further complicate the ethical landscape; with nurses finding themselves struggling with clinical ambiguity while advocating for patient rights, cultural safety, and equitable access.
Take Home Points
• Remote area nurses should critically interpret AI supported recommendations, while recognising how rural data gaps and historical marginalisation shape algorithmic bias.
• Transparency is AI-program design is essential—including how data is collected, aggregated, and used
• Data aggregation in remote regions is ethically fraught, with challenges in quality, consent, and security that directly affect patient outcomes.
• Equitable AI requires community engagement and context specific policy, ensuring rural and remote populations are not treated as “fringe cases” in health data systems