Algorithms can help diagnose a growing range of health problems, but humans need to be trained to listen.
Nurse Dina Sarro didn’t know much about artificial intelligence when Duke University Hospital installed machine learning software to raise an alarm when a person was at risk of developing sepsis, a complication of infection that is the number one killer in US hospitals. The software, called Sepsis Watch, passed alerts from an algorithm Duke researchers had tuned with 32 million data points from past patients to the hospital’s team of rapid response nurses, co-led by Sarro.
But when nurses relayed those warnings to doctors, they sometimes encountered indifference or even suspicion. When docs questioned why the AI thought a patient needed extra attention, Sarro found herself in a tough spot. “I wouldn’t have a good answer because it’s based on an algorithm,” she says.
Sepsis Watch is still in use at Duke—in no small part thanks to Sarro and her fellow nurses reinventing themselves as AI diplomats skilled in smoothing over human-machine relations. They developed new workflows that helped make the algorithm’s squawks more acceptable to people.
A new report from think tank Data & Society calls this an example of the “repair work” that often needs to accompany disruptive advances in technology. Coauthor Madeleine Clare Elish says that vital contributions from people on the frontline like Sarro are often overlooked. “These things are going to fail when the only resources are put towards the technology itself,” she says.
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