بهینه‌سازی زمان‌بندی جراحی انتخابی مبتنی بر پیش‌بینی اشغال تخت‌های بخش مراقبت‌های ویژه-مطالعه موردی یک بیمارستان آموزشی درمانی در اصفهان

Introduction: The operating room is one of the major centers of cost and revenue in hospitals, and its effective management can improve hospital performance and facilitate the timely delivery of healthcare services. In many hospitals, the intensive care unit(ICU) acts as a bottleneck, and its limited capacity may result in delays in or cancellation of elective surgeries.

Methods: This study proposes an optimization framework based on ICU-capacity forecasting for scheduling elective surgeries. To determine surgeons’ surgery-days with the aim of balancing ICU-bed-occupancy by elective surgical-patients across weekdays, the number of ICU-beds occupied by emergency-patients on different days of the week is forecasted, and the remaining capacity for elective surgical-patients is determined accordingly. This descriptive and analytical study was conducted using real hospital data from a Hospital in Isfahan, comprising 12,836 ICU-records and 12,989 elective-surgery-records collected from 2019 to 2022. Subsequently, a linear-goal-programming(LGP) model was developed to determine surgeons’ surgery-days and allocate their required ICU-capacity.

Results: The results showed that, during the periods examined, the schedule generated by the model reduced the peak ICU occupancy by elective surgical-patients by 5.5% to 20%. Sensitivity analysis also showed that forecasting errors in the number of ICU-beds occupied by emergency-patients affected ICU capacity allocation and the extent of changes in the surgical schedule.

Conclusion: The proposed framework, by forecasting the capacity occupied by emergency-patients in the ICU, can contribute to a more balanced distribution of ICU occupancy by elective surgical-patients across surgery days while maintaining the constraints associated with high-priority surgeons.

Key Message: With a reliable forecast of available ICU capacity, the proposed mathematical model can serve as an effective decision-support tool for scheduling surgeons’ surgery-days and planning their required ICU capacity, thereby balancing ICU-bed occupancy by elective surgical-patients across the days of the week.