Summary
The article discusses the challenges of centralizing health care systems while using AI, which works best with decentralized data and decision-making. Centralized systems may slow down innovation and learning because they're not as adaptable or quick to react as AI needs. Using Canada as an example, the article explains how centralization can limit the effectiveness of AI in health care.
Key Facts
- Governments are centralizing health care systems by grouping hospitals, finances, and data.
- AI, a powerful technology, works best when it can access diverse and local data.
- Centralized systems can create an "intelligence bottleneck," where insight doesn't flow back to where care happens.
- AI improves through continuous learning and adapting to real-world data.
- Canada's Quebec province has strongly centralized its health care, reducing local decision-making.
- Ontario has tried to innovate with Ontario Health Teams, but real decision-making power is limited.
- AI can help customize care to different needs if local teams can make decisions based on the data.
- Centralization is sometimes justified by value-based care, but value can be different for each community.
