Machine learning has the potential to drastically improve efficiency and the quality of care in hospitals by tackling hard-to-predict problems like ICU occupancy or which patients are likely to be readmitted.

Yet, a big barrier to any technology working optimally is getting full buy-in from its users, particularly when they’re busy medical workers who rely on good information to make split-second decisions that affect their patients’ health. Machine learning tools, which use artificial intelligence to improve the accuracy of their analysis, may be seen with skepticism.

“Machine learning is a technology that’s not well understood and thus not well trusted,” says Sara Singer, a professor of organizational behavior (by courtesy) at Stanford Graduate School of Business, and a professor of medicine at Stanford University School of Medicine. “People describe it as a black box—they feel like they don’t have input into how it’s used. It has an ability to add value, but only if we can create trust.”

 …