AI IN SMARTER PATIENT FLOW
Discussion Points ▪ Patient flow encompasses movement through healthcare systems from pre-assessment to discharge, including information flow ▪ Current challenges include fragmented systems, lack of data standardisation, and limited interoperability between public/private sectors ▪ Hegemonic application of healthcare systems often fails to incorporate Treaty of Waitangi principles and cultural needs ▪ Data sharing barriers exist between regions (e.g., South Island vs North Island patient information) ▪ Opportunities exist for AI to assist with referral management, discharge planning, bed management, and resource allocation ▪ Potential for AI to help patients navigate complex healthcare systems through "AI navigators" or agents Key Actions ▪ Explore AI applications for triage and referral management to direct patients to appropriate services ▪ Consider AI tools for predicting patient acuity and resource needs to improve bed management ▪ Investigate ambient AI scribes to reduce documentation burden on clinicians ▪ Look into AI-supported discharge planning to speed up patient flow and reduce bottlenecks ▪ Develop standardised definitions for data collection to enable effective AI implementation Need for balance between AI assistance and human clinical judgment ▪ Importance of co-design with patients and communities to ensure culturally appropriate solutions ▪ Consideration of privacy, security and consent frameworks for data sharing ▪ Workforce development and training required for effective AI implementation ▪ Potential for ACC framework to address liability concerns with AI clinical decision support ▪ Opportunity for New Zealand to lead in healthcare AI due to unique NHI system and ACC model Additional Notes ▪
Table sponsored by
Supported by St Georges Hospital
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