Unlocking Efficient Hospital Operations: Discrete Event Simulation in R

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Abstract In healthcare systems, optimizing resource allocation without compromising patient care is paramount. This study employs a simulation-based approach to evaluate the efficiency of bed allocation within a hospital setting. Utilizing a patient arrival model with an exponential distribution, we simulated patient trajectories to examine system bottlenecks, particularly focusing on waiting times. Initial simulations painted a scenario of an “unstable” system, where waiting times and queue lengths surged due to the limited number of available beds. Through iterative simulations, we explored the operational research question: “What is the minimum number of beds required to stabilize the system?” Our results, visualized in a series of detailed metrics plots, suggest that the addition of a specific number of beds can significantly reduce patient waiting time and stabilize the system. This research offers insights for hospital management on resource optimization, potentially leading to improved patient care and reduced operational costs.

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