This study aims to develop a predictive monitoring system that provides real-time estimates of service demand for key units within an Emergency Department (ED) and warns managers of any potential emerging critical situation. Employing advanced machine learning techniques and harnessing real-time data from a medium-sized Italian ED, the proposed system forecasts service demands for the visit and treatment unit, radiological unit, and laboratory for the forthcoming hour. This innovative system allows for the continuous monitoring of key ED units and enables dynamic management of associated activities and resources, thereby aiding in the effective management of ED overcrowding.
A Predictive Monitoring System for Estimating Service Demand in Emergency Departments
Davide Aloini;Elisabetta Benevento
;Alessandro Stefanini
2024-01-01
Abstract
This study aims to develop a predictive monitoring system that provides real-time estimates of service demand for key units within an Emergency Department (ED) and warns managers of any potential emerging critical situation. Employing advanced machine learning techniques and harnessing real-time data from a medium-sized Italian ED, the proposed system forecasts service demands for the visit and treatment unit, radiological unit, and laboratory for the forthcoming hour. This innovative system allows for the continuous monitoring of key ED units and enables dynamic management of associated activities and resources, thereby aiding in the effective management of ED overcrowding.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
