Carolyn Busby 1, Emma Pienaar 1,2, Professor Michael Carter 1, Michael Caesar 2, Andre D’Penha 2
1 Center for Healthcare Engineering, University of Toronto
2 Data Science Program, University Health Network
This poster presents the development and application of a hospital-wide patient flow model that can be used for strategic and operational decision making.
Many Canadian hospitals run at or near capacity, frequently experiencing congestion due to surges in demand. “Surge Protocols” that formally define when and what kind of operational steps can be taken to alleviate congestion are routinely in use. Decisions across the hospital, regarding bed capacity and allocation, staffing levels, and the surgical block schedule influence the frequency and severity of congestion, which in turn manifests as high bed occupancy, delayed admissions, a crowded Emergency Department, surgical cancellations and increased use of surge protocols.
A generic, data-driven, discrete event simulation is presented that helps hospitals assess the impact of hospital wide decisions and surge policies on each area of the hospital. The operational model was developed in cooperation with two hospitals, and then applied at two additional hospitals. This model was then used to create a predictive model to evaluate short-term capacity requirements at the University Health Network’s Toronto General Hospital.
The predictive model is initialized from the actual bed occupancy and the planned elective surgical slate. Arriving patients are randomly generated from historical data to simulate future bed occupancy over the forecasting window. Given a forecast of the hospital state, the hospital can avoid the use of costly surge protocols by proactively discovering and planning for events such as increased demand in the near future.