Forecasting Patient Flow Pressures

At St. Michael’s Hospital (SMH), having insight into patient flow throughout the hospital is essential to resource planning and operational efficiency. When patients are admitted, discharged, or transferred in the hospital, several actions need to be taken to ensure patients receive timely care and resources do not become backlogged. Improving patient flow reduces wait times, improves operational efficiency, and ultimately improves care. However, due to reasons like the epidemic outbreak, traditional methods of controlling the flow of patients are no longer effective. In this project, Unity Health Toronto is seeking to find better way to control the patient flow, efficient by utilizing state-of-the-art machine learning models.

Faculty Supervisor:

Igor Jurisica

Student:

Partner:

Unity Health Toronto

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program: