An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making

TL;DR

This paper presents a multi-horizon time series forecasting framework using deep learning models to predict emergency department boarding times, integrating real-world and external data for proactive operational decision making.

Contribution

It introduces a novel forecasting framework with an MLOps web application prototype, enhancing ED congestion management through integrated data and advanced models.

Findings

Decomposition-based Linear and NLinear models outperform others across multiple horizons.

Models perform well even under extreme congestion scenarios.

The MLOps prototype facilitates practical implementation and continuous improvement.

Abstract

Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients remain in the ED while awaiting inpatient bed placement, is a key indicator of this congestion. Predicting ED boarding time in advance enables proactive operational decision making before congestion escalates. We developed and evaluated a multi-horizon time series forecasting framework to predict ED boarding time at 6, 8, 10, 12, and 24-hour horizons. Real-world data from a university-affiliated urban hospital in the United States were utilized and integrated with external contextual data sources, including weather, holidays, and major local events. Decomposition-based Linear (DLinear) and Normalization-based Linear (NLinear) time series forecasting deep learning models showed superior performance across multiple horizons. Models were also evaluated under extreme congestion scenarios characterized by elevated boarding times. In addition, a Machine Learning Operations (MLOps) web application prototype was developed to support translation of the forecasting framework into practice through integrated data ingestion, forecast visualization, experimentation, and retraining.