AI IN HEALTH EQUITY AND ACCESS
Discussion Points ▪ Scope of discussion expanded beyond disability to include mental health, rainbow, migrant, Māori, Pasifika, rural and other vulnerable groups ▪ Digital poverty remains a significant barrier to equitable AI access - many communities lack basic hardware, internet connectivity, and digital literacy ▪ Data quality concerns - marginalised groups often have poor representation in datasets used to train AI systems ▪ Trust issues between vulnerable communities and health systems may extend to AI tools ▪ Privacy and data sovereignty concerns, particularly for Māori and vulnerable populations ▪ Potential for AI to reduce administrative burden on healthcare workers, allowing more time for patient care ▪ Need for New Zealand-specific AI solutions that reflect Te Tiriti o Waitangi principles and local context ▪ Concerns about AI being seen as a replacement for human services rather than augmentation Key Actions ▪ Focus on improving basic digital infrastructure and literacy before implementing advanced AI solutions ▪ Develop clear guidelines for AI use in healthcare settings, including transparency about when AI is being used ▪ Ensure AI tools are designed with accessibility features from the beginning, not as afterthoughts ▪ Involve people with lived experience in AI development from inception, with fair compensation for their expertise ▪ Create mechanisms for AI to help reduce repetitive trauma storytelling for patients ▪ Establish New Zealand-specific data repositories to improve AI training on local populations ▪ Develop education programmes about AI risks and benefits for both healthcare workers and patients Additional Notes ▪ Comparison made between experimental medicines (which carry clear warnings) and AI tools (which often don't) ▪ Discussion of urban narcissism/geographical narcissism affecting rural healthcare access ▪ Potential for AI to help with navigation of complex health systems, particularly for those with communication difficulties ▪ Opportunity for AI to improve continuity of care through better record-keeping ▪ Need to measure meaningful outcomes that matter to patients (relationships, quality of life) rather than just system metrics ▪ Concerns about commercial interests creating siloed systems that don't communicate with each other ▪ Recognition that younger generations have different perspectives on privacy and technology use
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