Abstract
BackgroundPatients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number of ORs needed to minimize patient wait times while optimizing resources.MethodsWe used patient arrival data and surgical procedure length from our institution, a tertiary-care academic medical center that serves a large diverse population. With ~4800 patients/year requiring non-elective surgery, and mean procedure length 185 min (median 150 min) we determined the number of ORs needed during the day and evening (0600–2200) and during the night (2200–0600) that resulted in acceptable wait times.ResultsSimulation of 4 ORs at day/evening and 3 ORs at night resulted in median wait time = 0 min (mean = 19 min) for emergency cases requiring surgery within 2 h, with wait time at the 95th percentile = 109 min. Median wait time for urgent cases needing surgery within 8–12 h was 34 min (mean = 136 min), with wait time at the 95th percentile = 474 min. The effect of changes in surgical length and volume on wait times was determined with sensitivity analysis.ConclusionsMonte Carlo simulation can guide decisions on how to balance resources for elective and non-elective surgical procedures.
Highlights
Patients often wait to have urgent or emergency surgery
Administrative data for surgical procedures performed at University of California Davis Medical Center (UCDMC) were used to determine: 1) the “arrival” rate of patients requiring urgent and emergency surgical care in the Pavilion operating rooms (ORs), which was defined as the time when a schedule request was submitted; 2) the length of the surgical procedure
The distribution of inter-arrival times are shown in Fig. 1 for real data for 1 year (2013) at UCDMC and for simulated data using the Monte Carlo simulation
Summary
Patients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number of ORs needed to minimize patient wait times while optimizing resources. Queuing theory ( known as waiting-line modeling) and other operational research techniques have been used in a variety of healthcare settings to determine how long patients must wait for care relative to the available resources [2,3,4,5,6,7]. We describe a model that predicts the waiting time for patients needing urgent surgical care. We used standard queuing theory models and Monte Carlo techniques to test the validity of our findings and predictions
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