Emergency Department Optimization and Load Prediction in Hospitals

TL;DR

This paper presents a machine learning-based tool for predicting emergency department patient volume to improve resource allocation, demonstrating real-world deployment and insights into operational challenges.

Contribution

It introduces a novel predictive model for ED load forecasting and discusses its implementation and impact in a clinical setting.

Findings

Accurate ED volume forecasts improve resource planning.

Deployment revealed practical challenges in clinical settings.

User feedback enhanced model usability.

Abstract

Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are strained by such increases in patient volume. Accurate forecasting of incoming patient volume in emergency departments (ED) is crucial for efficient utilization and allocation of ED resources. Working with a suburban ED in the Pacific Northwest, we developed a tool powered by machine learning models, to forecast ED arrivals and ED patient volume to assist end-users, such as ED nurses, in resource allocation. In this paper, we discuss the results from our predictive models, the challenges, and the learnings from users' experiences with the tool in active clinical deployment in a real world setting.