Solving Patient Scheduling Problems through Analytics
By Rania Nasrallah-Massaad
The need for improved scheduling in outpatient and community settings
Scheduling is a central part of health care services, whether it deals with initial or follow-up visits in an outpatient setting or scheduling regular nurse visits in community care settings. Many variables need to be considered in planning the most effective time-saving and cost-saving approach. Major scheduling challenges lead to long wait-times for millions of patients. For example, the scheduling of outpatient visits can coincide with the arrival of new patients with competing priorities who need to be factored into the schedule, unavailability of the needed physician to meet medically recommended target times, and many others.
How can the optimal timing and frequency of these visits be ensured? Clearly current scheduling policies will need renewal to deal with issues of increased demand and limited capacity in health care services so that we can offer more patients access to care in a timely manner. Analytics can help health care organizations improve the policies and procedures currently in place that regulate scheduling.
What is the research about?
Professor Jonathan Patrick received a Discovery grant by the Natural Science and Engineering Research Council to optimize analytic models for improved scheduling, planning, and decision-making challenges in various health care settings.
Project title: Shared Computing Facility for Optimization and Modeling
Who will gain from this research?
Professor Patrick’s work will help health care organizations improve policies and better manage time and cost allocations by solving complex scheduling and capacity planning problems. Altogether, this work will benefit: 1) the patient by ensuring required services are provided in a timely manner, and 2) the health care system by improving efficiency and reducing cost of services and resources.