A Monte Carlo Simulation Approach to Optimizing Capacity in a High-Volume Congenital Heart Pediatric Surgical Center

Abstract

ImportanceElective surgeries are primarily scheduled according to surgeon availability with less consideration of patients' postoperative cardiac intensive care unit (CICU) length of stay. Furthermore, the CICU census can exhibit a high rate of variation in which the CICU is operating at over-capacity, resulting in admission delays and cancellations; or under-capacity, resulting in underutilized labor and overhead expenditures.ObjectiveTo identify strategies to reduce variation in CICU occupancy levels and avoid late patient surgery cancellation.DesignMonte Carlo simulation study of the daily and weekly CICU census at Boston Children's Hospital Heart Center. Data on all surgical admissions to and discharges from the CICU at Boston Children's Hospital between September 1, 2009 and November 2019 were included to obtain the distribution of length of stay for the simulation study. The available data allows us to model realistic length of stay samples that include short and extended lengths of stay.Main OutcomesAnnual number of patient surgical cancellations and change in average daily census.ResultsWe demonstrate that the models of strategic scheduling would result in up to 57% reduction in patient surgical cancellations, increase the historically low Monday census and decrease the historically higher late-mid-week (Wednesday and Thursday) censuses in our center.Conclusions and RelevanceUse of strategic scheduling may improve surgical capacity and reduce the number of annual cancellations. The reduction of peaks and valleys in the weekly census corresponds to a reduction of underutilization and overutilization of the system.

Let's estimate all parameters as probabilities: Precise estimation using Chebyshev's inequality, Bernoulli distribution, and Monte Carlo simulations

“…I N THIS work, we focus on the estimation of parameters that are of a non-probabilistic fashion, e.g., simulated estimates of claim amounts in actuaries [1] or simulated numbers of patients at risk of disease recurrence [2]. Typically, these parameters are hardly analytically derivable, thus estimated using Monte Carlo simulation and the following logic [3].…”