A study on policy decisions to embed flexibility for reactive recovery in the planning and scheduling process in operating rooms

The pursuit for achieving operational excellence in the operating room (OR) department is hampered by the uncertainty characterising the demand for healthcare resources. Incorporating this uncertainty is complicated because planning and scheduling decisions involving both resources and patients are typically organised according to a hierarchical decision structure in different phases. In this study, we link strategic, tactical, and operational decision-making and investigate the impact of policy decisions providing flexibility in the OR planning and scheduling process for reactive recovery to improve the operational outcome, achieving a trade-off between efficiency and consistency. We consider a sequential but interrelated proactive–reactive decision framework that is guided by both generic assumptions stated in the literature and real-life practice, mimicking all decision phases by relying on optimisation and trace-drive simulation. The analysis is performed via computational experimentation on a real-life dataset by varying the parameter settings associated with the availability of open OR capacity, clustering of surgeons, and duration of OR blocks. We compare the performance of well-known scheduling frameworks, namely, open booking and (modified) block booking, and provide insights regarding the necessary conditions to efficiently manage OR resources. The results demonstrate that constructing a master surgery schedule or an advance patient planning improves schedule consistency, whereas the re-scheduling of OR resources is indispensable to attain high OR utilisation. Insights are provided into the impact of different policy decisions that introduce flexibility in the OR planning and scheduling process to increase responsiveness to demand variability. To achieve satisfactory results, a certain degree of flexibility should be embedded via at least one of the policy decisions. However, together with potentially higher efficiency, higher variability in performance is observed such that different policy decisions should be attuned to each other.