Can the use of AI and predictive modeling speed up long waits at emergency rooms?

Pronounced staff shortages and a lack of hospital beds have led to prolonged wait times for patients in emergency rooms around the United States, recent studies show. Those studies also show that patient congestion is one of the main factors threatening efficiency, safety and quality of care.

Now two researchers, including one from the College of Applied Health Sciences at the University of Illinois, are looking at ways to reduce emergency care wait times.

Hyojung Kang, an assistant professor in the Department of Kinesiology and Community Health in AHS, is a co-investigator on a nearly $100,000 Jump ARCHES grant to develop innovative models aimed at reducing wait times. The Jump ARCHES program is a collaboration between OSF Healthcare, the University of Illinois Urbana-Champaign and the University of Illinois College of Medicine Peoria. It was established in 2014 by a $62.5 million gift to provide direct access and competitive grants to engineers and physicians working together to combat problems in the realm of health care.

Kang’s co-investigator is William Bond, an emergency department physician at OSF HealthCare Saint Francis Medical Center in Peoria. 

“To acknowledge that suffering [in the waiting room] is to use compassion, which is part of us at OSF HealthCare, and to address those needs as quickly as we can; to acknowledge that timeliness is part of the quality of care and we really want to have as timely of care as we can for our emergency department patients,” Bond said.

Improving time to treatment

The project is called: STREAM-ED: Simulation to Refine, Enhance and Adapt Management of Emergency. The team is creating models to predict short-term, mid-range and long-term demand using historic data in de-identified electronic medical records. The goal is to combine machine learning prediction, discrete event simulation (a method to test processes and interventions ideally prior to intervention) and optimization techniques to determine best possible operational changes in emergency department management.

Kang specializes in discrete simulation, which provides a layered analysis of non-linear relationships among factors such as patient flow, availability of resources and operational policies that influence where patients are placed and for how long. The process provides a more comprehensive understanding of the way the system performs.

“Discrete-event simulation is a powerful technique used to model and analyze dynamic behaviors of complex systems, such as emergency departments,” Kang said. “In an ED DES model, individual entities like patients are simulated, along with their interactions with various resources like physicians and nurses.”  

Kang says the EMR information leveraged by researchers to create predictive models includes chief complaints, acuity levels, whether a patient was discharged, and timestamps collected throughout the patient’s time in the emergency department. They’ll also use data about physical resources and providers, including nurses and technicians who deliver assessments or care in different pods within the emergency department.

Bond says it also offers a way of testing interventions and timing without having to do it in real life. 

“Instead, we may find that staffing an area with a more balanced team is the thing to do, staffing the team earlier in the day or later in the day. These types of things may make significant changes in our ability to care for patients.”

Running those scenarios will help identify high-reward interventions that can make the biggest impact with the fewest resources to increase efficiencies that can also help providers from feeling burnt out.

There have been studies that use forecasting and modeling approaches in the past, but Kang says their practical application and integration into real-world operations have been limited. The project should result in helping decision-makers understand feasible actions they can take to improve emergency department flow.

“Our research team aims to empower [emergency department] leaders with the necessary, data-informed tools to navigate the complexities of resource allocation, making a tangible difference in the daily functioning of the ED,” Kang said.

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