Early Warning Software for Emergency Department Crowding.

Area of Science:

• Health Services Research
• Predictive Analytics
• Emergency Medicine

Background:

• Emergency department (ED) crowding is a significant patient safety concern, linked to increased mortality.
• Accurate demand forecasting is crucial for resource management and improving patient care, yet practical applications lag behind research.
• Existing research highlights the need for real-world implementation of predictive models for ED crowding.

Purpose of the Study:

• To present the initial findings of a prospective early warning software for predicting emergency department crowding.
• To evaluate the software's ability to generate real-time crowding predictions.
• To assess the accuracy of short-term and medium-term crowding forecasts.

Main Methods:

• A prospective crowding early warning software was developed and integrated with hospital databases.
• Real-time predictions were generated hourly over a 5-month period in a Nordic combined ED.
• Holt-Winters' seasonal methods and simple statistical models were employed for forecasting.

Main Results:

• The software achieved high accuracy in predicting next-hour crowding (AUC = 0.94).
• Predictions for 24-hour crowding also showed significant accuracy (AUC = 0.79).
• Afternoon crowding was predictable at 1 p.m. with an AUC of 0.84.

Conclusions:

• The developed software demonstrates strong predictive capabilities for ED crowding.
• Real-time forecasting can support proactive resource management in emergency departments.
• This approach has the potential to mitigate risks associated with ED crowding and improve patient safety.