Adopting a hierarchical diagnosis and treatment system to optimize elective surgery scheduling

The COVID-19 outbreak has changed the hospital demand dynamics by increasing the ICU/ward demand and uncertainty. While traditional standalone hospitals have struggled to manage their elective surgery scheduling during the pandemic, hierarchical diagnosis and treatment systems (HDTS) with the high- and low-level hospitals have had some advantages as the high-level hospitals can transfer postoperative recovery patients to their low-level hospital in the network to mitigate the resource shortages. However, in practice, this task is challenging as patient transfers could be costly depending on the patient’s condition, transfer time, and transfer hospital. Thus, there exists an interesting tradeoff between the costs and benefits associated with the patient transfer process within an HDTS. Since data is usually not available in this context, we develop a fuzzy (based on executive opinion) scheduling model for elective surgeries considering the tradeoffs in the patient transfer process. An efficient hybrid algorithm based on the genetic algorithm, variable neighborhood search, and heuristic rules is proposed to cope with the computational complexity of the problem, and the adaptability of the fuzzy model in an uncertain environment is validated. Our paper highlights how hospitals can maximize their profits by transferring patients in an HDTS when the demand for ICUs/wards is uncertain, and thus, this framework also applies to elective patient scheduling during epidemic outbreaks such as COVID-19.