Epic, the nation's dominant seller of electronic health records, was bracing for a catastrophe. It was June 2021, and a study about to be published in the Journal of the American Medical Association had found that Epic's artificial intelligence tool to predict sepsis, a deadly complication of infection, was prone to missing cases and flooding clinicians with false alarms. Reporters were clamoring for an explanation. Unlock this article by subscribing to STAT and enjoy your first 30 days free!

Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives. Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling.

Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives. Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling.