Robot Helps Nurses Schedule Tasks on Labor Floor

Estimated reading time: 5 minutes

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, and that robots might be most effective by helping humans perform one of the most complex tasks of all: scheduling.

In a pair of new papers, CSAIL researchers demonstrate a robot that, by learning from human workers, can help assign and schedule tasks in fields ranging from medicine to the military.

In one paper, the team demonstrated a robot that assisted nurses in a labor ward, where it made recommendations on everything from where to move a patient to which nurse to assign to a C-section.

“The aim of the work was to develop artificial intelligence that can learn from people about how the labor and delivery unit works, so that robots can better anticipate how to be helpful or when to stay out of the way — and maybe even help by collaborating in making challenging decisions,” says MIT professor Julie Shah, the senior author on both papers.

In a second paper, the same system was put to the test in a video game that simulates missile-defense scenarios. In the game, which was developed by MIT Lincoln Laboratory researchers and involves using decoy missiles to ward off enemy attacks, the system even occasionally outperformed human experts at reducing both the number of missile attacks and the overall cost of decoys.

The labor-ward paper was presented at the recent Robotics: Science and Systems (RSS) Conference and was co-written by PhD student Matthew Gombolay, CSAIL postdocs Xi Jessie Yang and Brad Hayes, and Neel Shah and Toni Golen from Beth Israel Deaconess Medical Center, which is where the study took place.

The Navy-simulation paper is being presented at this week’s International Joint Conference on Artificial Intelligence (IJCAI), and was co-written by Gombolay and the Lincoln Lab’s Reed Jensen, Jessica Stigile, and Sung-Hyun Son.

Right on schedule 

From visiting hospitals and factories, Shah and Gombolay found that a subset of workers are extremely strong schedulers, but 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, how long labor will take, and which patients will become sick enough to require C-sections or other procedures.

They are deluged with an endless stream of challenging split-second decisions that include assigning nurses to patients, patients to beds, and technicians to surgeries. At Beth Israel, the head nurse has to coordinate 10 nurses, 20 patients, and 20 rooms at the same time, meaning that the number of distinct scheduling possibilities adds up to a staggering 21,000,000, which is more than the number of atoms in the universe.

“We thought a complex environment like a labor ward would be a good place to try to automate scheduling and take this significant burden off of workers,” says Gombolay.

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