Innovative Models Improve Surgery Scheduling and Capacity Management

Recent advancements in surgical planning have introduced new models aimed at overcoming key challenges in hospital operations, particularly those related to the timing and scheduling of surgeries, capacity planning, and patient recovery processes. In collaboration with an active hospital environment, researchers have developed an integrated elective surgery assignment, sequencing, and scheduling problem (ESASSP). This model helps optimize the allocation of operating rooms, ICU beds, and recovery wards, addressing the complexities of variable surgery durations and patients' lengths of stay.

Implementing these innovative approaches could significantly reduce congestion in recovery units, minimize delays in operating room schedules, decrease overtime work, and lower operational costs. The study, conducted by experts from Carnegie Mellon University, USC, Texas Tech University, and the Medical University of South Carolina, was published in the European Journal of Operational Research.

Professor Rema Padman of Carnegie Mellon’s Heinz College emphasizes the practical impacts of these models, which take into account the uncertainties in surgery durations and recovery times. The main challenges addressed include coordinating multiple high-cost resources with limited capacities, managing unpredictable surgery times, and making critical decisions about the assignment of surgeries to optimize resource utilization.

The researchers proposed and analyzed distributionally robust optimization (DRO) approaches and stochastic programming models to minimize costs while accommodating variability in surgical procedures and patient recovery times. Their computational analysis of real-world data revealed that adopting such integrated models can lead to a 24% to 60% reduction in total costs for hospitals performing elective surgeries.

Additionally, the models shed light on the trade-offs between increasing surgical volume and operational performance, highlighting the importance of data-driven decision-making in dynamic hospital environments. While promising, the researchers note that further development of practical tools and training is necessary for hospitals to effectively adopt these models, especially given the lack of specialized staff trained in optimization techniques.

Overall, this research provides a significant step forward in hospital management, enabling more efficient use of limited resources, improving patient care, and reducing operational bottlenecks.

Source: https://medicalxpress.com/news/2025-09-problems-surgeries-capacity-patients-stays.html