AI’s chaotic rollout in big US hospitals detailed in anonymous quotes

Enlarge (credit: Aurich Lawson | Getty Images)

When it comes to artificial intelligence, the hype, hope, and foreboding are suddenly everywhere. But the turbulent tech has long caused waves in health care: from IBM Watson’s failed foray into health care (and the long-held hope that AI tools may one day beat doctors at detecting cancer on medical images) to the realized problems of algorithmic racial biases.

But, behind the public fray of fanfare and failures, there’s a chaotic reality of rollouts that has largely gone untold. For years, health care systems and hospitals have grappled with inefficient and, in some cases, doomed attempts to adopt AI tools, according to a new study led by researchers at Duke University. The study, posted online as a pre-print, pulls back the curtain on these messy implementations while also mining for lessons learned. Amid the eye-opening revelations from 89 professionals involved in the rollouts at 11 health care organizations—including Duke Health, Mayo Clinic, and Kaiser Permanente—the authors assemble a practical framework that health systems can follow as they try to roll out new AI tools.

And new AI tools keep coming. Just last week, a study in JAMA Internal Medicine found that ChatGPT (version 3.5) decisively bested doctors at providing high-quality, empathetic answers to medical questions people posted on the subreddit r/AskDocs. The superior responses—as subjectively judged by a panel of three physicians with relevant medical expertise—suggest an AI chatbot such as ChatGPT could one day help doctors tackle the growing burden of responding to medical messages sent through online patient portals.