Probabilistic Forecasting of Patient Waiting Times in an Emergency Department

TL;DR

This paper develops a probabilistic model to estimate and dynamically update patient waiting time distributions in emergency departments, incorporating real-time patient and ED data to improve communication and operational efficiency.

Contribution

It introduces a feature-rich, dynamic forecasting method that refines waiting time estimates as new information becomes available, enhancing practical implementation and communication.

Findings

Dynamic updating improves forecast accuracy

Incorporating ED congestion levels enhances predictions

Method facilitates better patient communication

Abstract

We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Our feature-rich modelling allows for dynamic updating and refinement of waiting time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. Aspects relating to communicating forecast uncertainty to patients, and implementing this methodology in practice, are also discussed.