Case Study: University of Toronto Research
- NextUp Care collaborated with the University of Toronto Centre for Healthcare Engineering and two Ontario hospitals to evaluate the impact of a central scheduling policy on MRI outpatient wait times.
- The study showed that having a small percentage of patients available to travel was enough to reduce wait times when load balancing demand across the health system.
- The reduction of wait times was at the system level. From the hospital perspective, Hospital B, which had a higher wait time, showed a clear reduction. In contrast, Hospital A, which had a lower wait time, maintained the same average wait time. However, some patients that moved from Hospital A to Hospital B reduced their wait time, suggesting that even when hospitals do not reduce their overall wait time, it is still beneficial for those patients that travel to another location.
- Overall, the results suggest that an algorithmic-based central scheduling policy can improve operational efficiency and reduce wait times in the health system.
White Paper: A Simulation based Approximate Dynamic Programming Approach to Multi-class, Multi-resource Surgical Scheduling
Astaraky, D. and Patrick, J. 2015. A Simulation based Approximate Dynamic Programming Approach to Multi-class, Multi-resource Surgical Scheduling.
White Paper: Estimating the Waiting Time of Multi-priority Emergency Patients with Downstream Blocking
Lin, D., Patrick, J. and Labeau, F. 2014. Estimating the Waiting Time of Multi-priority Emergency Patients with Downstream Blocking.
White Paper: A Markov Decision Model for Optimal Outpatient Scheduling
Patrick, J. 2012. A Markov Decision Model for Optimal Outpatient Scheduling.
White Paper: Dynamic Multi-Appointment Patient Scheduling for Radiation Therapy
Sauré, A., Patrick, J.,, Tyldesley, S. and Puterman, M.L. 2012. Dynamic Multi-Appointment Patient Scheduling for Radiation Therapy.