Nurses are getting help from robots with scheduling tasks on labor floor (Video)

Nurses are getting help from robots with scheduling tasks on labor floor. A new system that can anticipate room assignments and suggest which nurses to assign to patients for C-sections and other procedures provided satisfactory recommendations to nurses 90 percent of the time. Today’s robots are awkward co-workers because they are often unable to predict what humans need. In hospitals, robots are employed to perform simple tasks such as delivering supplies and medications.But they have to be explicitly told what to do.

A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) thinks that this will soon change. They also think that robots might be most effective by helping humans perform one of the most complex tasks of all.

From visiting hospitals and factories, Shah and Gombolay found that a subset of workers are extremely strong schedulers. But they can’t easily transfer that knowledge to colleagues.”Figuring out what makes certain people good at this often seems like a mystery,” Gombolay says. “Being able to automate the task of learning from experts — and to then generalize it across industries — could help make many businesses run more efficiently.”

A particularly tough place for scheduling are hospitals. Labor wards’ head nurses have to try to predict when a woman will arrive in labor. And how long labor will take, and which patients will become sick enough to require C-sections or other procedures.

Like many AI systems, the team’s robot was trained via “learning from demonstration,” which involves observing humans’ performances of tasks. Gombolay says researchers have never been able to apply this technique to scheduling. He said because of the complexity of coordinating multiple actions that can be very dependent on each other.

To overcome this, the team trained its system to look at several actions that human schedulers make. The team also has to compare them to all the possible actions that are not made at each of those moments in time. From there, it developed a scheduling policy that can respond dynamically to new situations that it has not seen before.


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