Key result
A stochastic mathematical model for patient admission scheduling outperformed deterministic models in assigning procedures to operating rooms under ambiguity regarding surgery length and emergency arrivals.
Why the study?
The growing volume of technical operating room scheduling literature calls for an evaluation of the field to help researchers respond to new trends, addressing the patient admission scheduling problem with stochastic arrivals and departures.
A stochastic computational model for operating room scheduling outperforms deterministic models and can be applied to large-scale real-world healthcare settings.
May support stochastic OR scheduling under uncertainty; leaves open prospective validation before clinical adoption.
Operating room scheduling is a prominent study topic due to its complexity and significance. The increasing number of technical operating room scheduling articles produced each year calls for another evaluation of the literature to enable academics to respond to new trends more quickly. The mathematical application of a model for the patient admission scheduling issue with stochastic arrivals and departures is the subject of this study. The approach for applying our model to real-world issues is discussed here. We present a solution technique for efficient computing, a numerical model analysis, and examples to demonstrate the methodology. This study looked at the challenge of assigning procedures to operate rooms in the face of ambiguity regarding surgery length and the arrival of emergency patients based on a flexible policy (capacity reservation). We demonstrate that the proposed methods derived from deterministic models are inadequate compared to the answers produced from our stochastic model using simple numerical examples. We also use heuristics to estimate the objective function to build more complicated numerical examples for large-scale issues, demonstrating that our methodology can be applied quickly to real-world situations that often include big information sets.
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Rad et al. (2022) studied Patient admission and operating room scheduling. Stochastic mathematical model for patient admission scheduling vs. Deterministic models was evaluated on Efficient computing and assignment of procedures to operating rooms. A stochastic mathematical model for patient admission scheduling outperformed deterministic models in assigning procedures to operating rooms under ambiguity regarding surgery length and emergency arrivals.
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