AI-POWERED INTEROPERABILITY, DRIVING EFFICIENCY OPTIMISATION, AND PREVENTING READMISSION
Summary ▪ Interoperability challenges in New Zealand healthcare systems, with fragmented data across primary care, hospitals, and community services ▪ Barriers to AI adoption including infrastructure limitations (poor WiFi, outdated systems), privacy concerns, and lack of standardised data formats ▪ Potential AI applications discussed: • Administrative task automation to free clinical staff for patient care • Discharge summary improvement and follow-up alerts • Patient recall systems and chronic disease management • Early intervention triggers based on known clinical pathways • Ambient AI for background monitoring without disrupting workflows Key Actions ▪ Need for centralised guidance on AI implementation while allowing bottom-up innovation ▪ Develop clear consent frameworks that balance patient privacy with clinical utility ▪ Improve vendor engagement through deeper workflow understanding before implementation ▪ Create multi-modal training approaches when introducing new technologies ▪ Consider a "constitution" of agreed standards for healthcare data and AI use Additional Notes ▪ Cultural resistance to standardisation identified as a significant barrier to healthcare integration in New Zealand ▪ Staff adoption requires transparent communication about implementation challenges ▪ Patient data sovereignty concerns particularly important for Māori and Pacific populations ▪ Successful AI implementation requires balancing immediate time-saving benefits with realistic expectations about transition periods ▪ Leadership support critical for creating organisational confidence in new technologies
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