Year: 2025
Type: article
Abstract: Background: The Emergency Department (ED) plays a vital role in the healthcare system, frequently operating under high demand and limited resources. Increasing patient volumes, constrained community support, and EMTALA obligations lead to operational challenges such as long wait times, resource strain, and staff fatigue. While existing tools aim to predict patient arrivals, they often lack the sophistication needed to forecast ED waiting room census accurately. To address this, the team developed a machine learning model (see Figure 1) to more precisely anticipate surge conditions and improve operational responsiveness.
Authors: Lindsey Asher, Greg Davis, Brendan M. Carr, James E. Colletti, Laura E. Walker, Derick D. Jones
Language: English
Subfield: Emergency Medical Services
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4417356534
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