September 30, 2021

Emergency rooms across the country are strained as they see an influx of people experiencing COVID-19 symptoms alongside patients needing other types of immediate care. Now, a Mizzou Engineering team is proposing a model that could help emergency departments better manage unexpected demand.

Associate Professor Ron McGarvey, Assistant Professor Kangwon Seo and PhD student Zeynab Oveysi — all in industrial and manufacturing systems engineering — are proposing a management system in which emergency departments would work together to optimize processes and maximize resources.

The model hinges on transferring emergency room patients between hospitals based on capacity and need.

“We want to balance the workload, so you don’t have situations where you have too many people in one emergency department waiting to be treated while you have an underutilized emergency department in another hospital at the same time,” said McGarvey, who has a joint appointment in the Truman School of Public Affairs.

The research team considered two main factors when developing the mathematical model. First, they considered how many beds each emergency room has based on a department’s ability to treat patients with staffing and equipment. They then considered parameters around which patients should be transferred from one hospital to another.

“There are patients in critical condition you wouldn’t want to transfer,” McGarvey said. “Then there are less critical patients, like a person who has a broken leg. Those are the types of decisions our model is trying to address.”

The system also calculates wait time, or the time it takes between arriving at the hospital and receiving care. And Oveysi and Seo provided analysis to factor in uncertainties such as when emergency rooms will see an increase in patients and how long each patient will need to be treated.

“Typically, there are many parameters involved in the model, so we ran the operation research optimization with some assumed values,” said Seo, who has a joint appointment in statistics. “In real practice, you’re not sure whether the parameter values will happen or not, so you try to fluctuate the parameters a bit to see what happens in an optimal solution.”

That’s the novelty of this paper, McGarvey said.

“We’re able to make recommendations accounting for uncertainty,” he said. “What we’re going to constrain in the model is both the waiting time in the system plus time in transfer. We don’t want people to have to spend a long time waiting to be seen or spend a long time being transferred between hospitals. So, given a limited budget to spend on capacity, what’s the best you can do in terms of managing time, so it’s not wasted? That’s what the model is going to do — look across almost infinite ways to arrange resources across hospitals.”

The research team published their findings last month in Advances in Operations Research.