A scalable methodology for optimizing hospital surgical schedules considering efficiency, flexibility, and improved patient outcomes

Hospitals face challenges in efficiently adapting treatment delivery to growing and changing demands. The main challenge arises from accommodating diverse patients requiring specific surgical resources and attention. Traditional scheduling methods often fail to address the dynamic nature of these environments, which are characterized by numerous uncertainties and stakeholders’ complex and changing needs. This study presents a novel methodology designed to enhance hospital operational efficiency while considering the interests of all stakeholders, including hospital administrators, medical staff (doctors, nurses, technicians), and patients. This requires a nuanced approach to effectively handle unpredictable treatment demands, resource availability, and patient requirements. The methodology systematically progresses from defining constraints and resources to modeling uncertainties generating and evaluating optimal schedules through iterative processes. This study develops and applies a 12-step method to optimize the surgery scheduling for the farm animal section of the Purdue Veterinary Hospital over a defined period. The application shows the practical benefits of the proposed approach by modeling dynamic surgical demands and exploring various scheduling possibilities within resource constraints. The results reveal that the proposed method effectively accommodates increased operational demands while managing delays, accidents, and illness costs. • Propose a method for optimizing surgical scheduling through systematic uncertainty modeling. • Enhance resource allocation by considering dynamic demands and stakeholder requirements. • Improve operational efficiency by minimizing delays and reducing treatment costs. • Demonstrate a scalable and flexible framework adaptable to diverse hospital environments. • Balance stakeholder needs while maintaining flexibility in resource management.