Emergency department waiting time predictions in real-time

Grant ID: EMJS-343R33-2020-STAIB

Lay Summary

Emergency department (ED) waiting times are a significant predictor of the patient experience.

Simple prediction methods, such as rolling average, are used by hospitals in Australia to predict waiting time for patients. Although this approach is inexpensive to implement, the forecasts have limited accuracy and consequently most Australian hospital EDs do not report expected waiting times to the public.

A solution that is capable of sourcing data from ED information systems and feed it into prediction models to generate waiting time forecasts would bring practical benefits for staff and patients. There is also potential to assist clinicians and nurses to estimate demand for care and calibrate workflow.

For patients, the knowledge may reduce anxiety associated with uncertainty about the waiting time and reduce the number of patients who leave before treatment.

This project aims to use advanced statistical models and machine-learning algorithms to capture dynamic fluctuations in waiting time, to implement and validate the prediction performance of these models. The project will also build ED research capacity by educating staff on forecast modelling and data management techniques.

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