It’s 2 a.m. in the ER, and a physician is asking the hospital’s new AI assistant to summarize a patient’s chart. The cursor spins for minutes while the AI tool waits on the decades-old database underneath to answer, something it was never designed to do.

The electronic health record (EHR) system is too old and was not built to keep up with AI’s demands. It’s like asking a 50-year-old runner to sprint with a 25-year-old; a pulled muscle or torn ligament is coming. Hospitals everywhere are operating this way as they onboard AI tools, not knowing whether their underlying systems can withstand the warp-speed demands of these technologies.

In another industry, a slow or sometimes-crashing system may create headaches for the sales team. In healthcare, patients are at risk. The doctor may have forgotten how to find the information “the old way” as the cursor spins and spins. The intersection of overloaded and aging systems, outsized patient needs, and today’s desire for speed and emergent technologies has the makings of a crisis. And it is a crisis nobody in healthcare is enthused to talk about or proactively solve.

Healthcare AI comes with massive upside; insights are frequently published touting how AI is making healthcare more prescriptive, efficient, and cheaper. It can help with workforce shortages and faster documentation, while providing speedy answers and shrinking time spent navigating prior authorizations. , which also has its own roadmap of AI features . Major national insurers are embedding solutions from those same AI companies onto their platforms.

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