A Robust Optimization Approach for Advance Scheduling in Health Care Systems with Demand Uncertainty: Policy Insights
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Patient wait times have increased significantly over the past few decades. According to the Canadian Institute for Health Information (CIHI), 40% of Canadians have experienced difficulties in receiving diagnostics tests. MRI wait times have increased by 26% from 2012 to 2016. The lengthy wait times for the health care systems are translated to economic losses and risks to the lives of Canadians. These inefficiencies in health care systems are an indication that health care infrastructure investment has not been able to keep pace with the increased demands. While building new health care infrastructure to create capacity may be the first solution that comes to mind, it is often not feasible due to budget limitations. Optimizing the use of the existing capacity is a more feasible and cost-effective solution to healthcare system inefficiencies. This research builds on previous literature and proposes a robust optimization method for a multi-priority multi-period advance scheduling problem with wait time target which is solved using a proposed adversarial-based algorithm. A sensitivity analysis is conducted to calibrate the model parameters. Several numerical examples are used to extract practical policy insights. The advantages of the robust model in comparison with the deterministic model are highlighted. It is shown that the robust modelling leads to policies that are easier to execute and are more suitable for policy planning purposes when compared to deterministic modelling.
Cite this version of the work
Nafise Niazi Shahraki (2021). A Robust Optimization Approach for Advance Scheduling in Health Care Systems with Demand Uncertainty: Policy Insights. UWSpace. http://hdl.handle.net/10012/16810