A recent report from Nordic Consulting reveals that while 70% of healthcare leaders feel confident in their AI governance frameworks, only 15% have established scalable infrastructure. The complexity of scaling AI in healthcare stems from underestimating ongoing management and data readiness needs. Kevin Erdal emphasizes the significance of effective data management and interoperability for AI success, stating that without the right data, promising models are likely to fail. The focus should be on foundational elements rather than just the appealing aspects of AI tools.
If organizations can't capture the right data, models will fail - no matter how promising the technology, he warned.
While there's significant enthusiasm about AI, scaling it across a healthcare enterprise is proving to be an incredibly complex process.
Many organizations underestimate the ongoing management needs of AI models, especially custom tools that consume high computing resources.
It's one thing to be able to store data, and it's another to be able to process data.
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