Predictive Analytics in Healthcare: Decision Support Systems for Emergency Departments

Department

University of Pisa - Department of Energy, Systems, Territory and Construction Engineering

Main research activities/topics/projects

The primary objective of this project proposal is to enhance the operational efficiency of Emergency Departments (EDs) by implementing cutting-edge Artificial Intelligence and Analytics methodologies (e.g., Machine Learning, Process Mining, Statistical Learning, and Process Simulation). This will be accomplished through real-time monitoring of care processes and the dynamic optimization of patient flows and ED resources. To this aim, the project intends: - To develop and empirically test novel Machine Learning models tailored for predicting service times and workloads within EDs – e.g., patient arrivals, radiological workloads, and X-ray service times - To devise a Decision Support System that allows to evaluate in real-time different ED configurations, in terms of resources and processes (e.g., fast tracks, See&Treat, re-routing of ambulance patients), based on the expected ED status. The project will leverage real datasets and field-testing thanks to the involvement of local health organizations.

Working language

English

Special entry requirements

Background in Operation Management and/or Computer Science, data analysis and basic programming skills

Duration in months (min-max)

PhD sandwich: 3-12
Post Doc: 3-12