AI – LEVERAGING DATA FOR IMPACT
Discussion Points ▪ Data quality and consistency challenges across healthcare systems in New Zealand ▪ Difficulties with data silos and integration between 42 different data warehouses nationwide ▪ Concerns about data privacy, sovereignty and sharing between organisations ▪ Bias in healthcare data, particularly regarding gender and ethnicity ▪ Challenges with standardisation of clinical coding and terminology ▪ Importance of localised data for AI models rather than importing overseas models ▪ Considerations around building, buying, or adapting AI solutions ▪ Workforce readiness and training needs for AI implementation ▪ Funding constraints for AI initiatives in healthcare Key Actions ▪ Consider starting small with AI implementations and iterating based on results ▪ Focus on explainable AI to build trust with clinicians and patients ▪ Investigate natural language processing for unstructured clinical notes ▪ Explore data disaggregation to address bias in AI models ▪ Develop governance frameworks for AI use that balance innovation with privacy ▪ Consider adapting existing AI models with local data rather than building from scratch ▪ Investigate standardisation of ethnicity data collection (minimum Level 2) ▪ Look into creating "sandbox" environments for researchers to access de-identified data Additional Notes ▪ Dedalus highlighted their AI product (Clinalytics Medical AI) which is embedded in workflow ▪ Several organisations are at early stages of AI implementation ("phase zero") ▪ Breast Cancer Foundation NZ is working on implementing AI for pathology report reading ▪ Concerns raised about the cost of AI implementation versus available funding ▪ Discussion about the need for continuous model retraining and validation ▪ Emphasis on AI as augmentation rather than replacement for clinical decision-making ▪ Noted that AI models need to be registered as medical devices in many jurisdictions
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