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Many organizations are trying to implement healthcare artificial intelligence (AI) projects using traditional infrastructure models, which isn’t working well, observed Pure Storage Global Healthcare Technology Strategy VP Josh Gluck.

By using a traditional infrastructure approach, organizations are running into scalability problems with their AI projects, Gluck told HITInfrastructure.com.

“We're finding that a lot of folks want to use AI, and they're trying to get the most out of their existing traditional infrastructures. Unfortunately, they're running into situations where it takes an inordinate amount of time to hone their algorithms. The results that come back have to be reinterpreted, or they have to rewrite the algorithm,” Gluck related.

One of the biggest AI pain points for healthcare organizations with traditional infrastructure is that they have data in isolated pockets throughout their IT environment. “Healthcare traditionally has built infrastructures for applications. It has not provided an infrastructure where the data can be accessed freely by research programs, analytics programs, or AI machine-learning programs,” he said.

A recent Gartner report backs up Gluck’s analysis. It found that the increasing complexities of AI and other advanced technologies could cause healthcare IT infrastructure to fall behind, resulting in a chaotic IT environment.

“As the advance of technology concepts continues to outpace the ability of enterprises to keep up, organizations now face the possibility that so much change will increasingly seem chaotic … The key is that CIOs will need to find their way to identifying practical actions that can be seen within the chaos,” said Gartner Fellow and Vice President Daryl Plummer.

Via Dominique Godefroy, Lionel Reichardt / le Pharmageek