The development of efficient surgical scheduling systems is becoming increasingly important as hospitals worldwide face rising patient demands and limited resources. A recent study tackles this issue by presenting a multi-resource constrained elective surgical scheduling (MESS) model utilizing the honey badger algorithm based on Nash equilibrium (HBA-NE) to improve operational efficiency.
The MESS problem incorporates the simultaneous management of material resources, such as operating rooms (ORs) and non-operatory room (NOR) beds, alongside human resources like surgeons and nurses. The study identifies the necessity of minimizing the average recovery time for patients, reducing the average overtime incurred by medical staff, and lowering the total costs associated with surgical operations. Through the implementation of the HBA-NE algorithm, researchers aim to create effective solutions within real hospital settings.
Elective surgeries account for about 40% of hospital expenditures, with over 60% of inpatient admissions linked to surgical procedures. Yet, traditional methods of scheduling tend to rely heavily on the personal schedules and preferences of surgeons, leading to inefficiencies and poor resource allocation. The study utilizes data from the First Affiliated Hospital of Hebei North University, analyzing the factors leading to suboptimal surgical schedules.
“The introduction of digital twin technology enables the integration of physical and virtual hospital resources for real-time surgical scheduling simulation,” one researcher noted, highlighting the advanced technology underpinning this study. Digital twins facilitate effective visual modeling of surgeries, allowing hospital managers to see how operations will flow before making decisions. This prepares them to handle potential disruptions such as surgical delays or resource shortages.
The HBA-NE algorithm incorporates innovative elements from both nature-inspired and game theory optimization methods, seeking to balance the often-conflicting interests of diverse stakeholders, including patients and medical staff. “Our method simultaneously optimizes recovery time, staff overtime, and overall medical costs, addressing complex scheduling faced by hospitals today,” the authors stated. Through this methodology, hospitals can streamline elective surgery processes significantly.
Research results indicate successful implementation of the proposed model, displaying marked improvements over previous scheduling techniques. The experimental studies revealed higher efficiency levels, with shorter recovery times and reduced overtime needs for surgical staff. The optimization not only enhances patient outcomes but also increases overall hospital revenue through effective resource management.
Case studies involving real hospital data have shown the practical performance of the HBA-NE algorithm, with accurate simulations aligning closely with actual surgical operations. Comparisons with traditional algorithms have substantiated claims of superior performance. “Using game theory principles allows us to balance the interests of patients, medical staff, and hospital management effectively,” the study emphasizes, showcasing the multifaceted benefits of integrating various methodologies.
Concluding the study, researchers suggest pathways for future exploration include the refinement of optimization algorithms and enhanced real-time decision-making strategies for smart hospitals. This integration of digital advancements and healthcare logistics is poised to revolutionize surgical scheduling, ensuring hospitals can meet increasing demands without compromising on the quality of patient care.