Piano Nazionale di Ripresa e Resilienza Missione 4 Componente 2, Investimento 1.1 Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN) - Avviso MUR pubblicato con Decreto Direttoriale n. 1409 del 14 settembre 2022 (PRIN 2022 PNRR) - progetto “P20222XM58 - Emergency medicine 4.0: an integrated data-driven approach to improve emergency department performances”, settore SH1
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.
Coordinator:
Prof. Alessandro Stefanini
Start date: 30 novembre 2023
End date: 30 novembre 2025
Duration: 24 months
WEBSITE: https://emanalytics4.math.unipd.it/
