Mitigating Emergency Department Crowding With Stochastic Population Models

TL;DR

This paper demonstrates that a stochastic population model effectively predicts emergency department crowding, enabling better forecasting and mitigation strategies by analyzing five-year detailed data.

Contribution

It introduces the application of a broad natural phenomena model to hospital ED crowding, providing reliable forecasts and insights into crowding dynamics.

Findings

Crowding is highly sensitive to patient arrival rates and length-of-stay.

A 10% increase in arrivals triples overcrowding probability.

Reducing length-of-stay by 20 minutes halves severe overcrowding events.

Abstract

Environments such as shopping malls, airports, or hospital emergency departments often experience crowding, with many people simultaneously requesting service. Crowding is highly noisy, with sudden overcrowding "spikes". Past research has either focused on average behavior or used context-specific non-generalizable models. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding, using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) reduces severe overcrowding events by 50%. Such forecasting is crucial in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many natural systems.