Emergency department (ED) overcrowding represents a global challenge that entails prolonged waiting times, extended length of stay, heightened clinical risks and stress for ED staff, overall patient dissatisfaction, and poorer care. To address this issue, the main goal of this project is to reduce ED overcrowding and enhance ED performances by implementing real-time process monitoring and dynamic management of patient flows and ED resources using advanced data analysis techniques such as Machine Learning, Process Mining, Statistical Learning, and Process Simulation.
Specifically, the project aims to achieve the following objectives:
– To develop and empirically test new Machine Learning (ML) models and techniques for time and workload prediction in EDs – e.g., patient arrivals, waiting times, and service times – to monitor ED processes in real-time.
– Assess the potential changes in ED patient arrivals resulting from: i) redirecting ambulance patients to less congested EDs within the same geographical area based on predicted ED workloads and performance metrics; ii) introducing Community Healthcare Centers (CHCs) to handle minor cases and diverting critical cases to less crowded EDs.
– Develop a simulation system customized for a single ED, allowing real-time evaluation of various ED configurations in terms of resource allocation and process flow (e.g., fast tracks, See&Treat), based on projected ED conditions such as service times and workloads.
The project leverages real-world datasets and field testing thanks to the involvement of hospitals and local health organizations.
The project consortium comprises an interdisciplinary team of researchers hailing from three prominent Italian universities: University of Pisa, University of Bergamo, and University of Padova. With backgrounds in management engineering and computer science, our research themes cover operations management, healthcare management, business process analytics, optimization models, business process management, and process mining.
Project funded by the European Union – NextGenerationEU under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.1 – Call PRIN 2022 PNRR No. 1409 of September 14, 2022 of Italian Ministry of University and Research; Project P20222XM58 (subject area: SH – Social Sciences and Humanities) “Emergency medicine 4.0: an integrated data-driven approach to improve emergency department performances”