<ul><li>Scheduling echocardiographic exams in a hospital is challenging due to non-deterministic factors and asymmetric resource constraints between fetal and non-fetal patient streams.</li><li>Researchers conducted preprocessing on operational data from Stanford University's Lucile Packard Children's Hospital to estimate patient no-show probabilities and derive empirical distributions of arrival times and exam durations.</li><li>A discrete-event stochastic simulation model was developed using SimPy and integrated with the Gymnasium Python library to evaluate different resource allocation strategies.</li><li>Results showed that on-the-fly allocation generally performed better in adapting to patient variability and resource constraints, leading to the development of a reinforcement learning-based optimal dynamic allocation policy for improving echo lab efficiency.</li></ul>

Optimal patient allocation for echocardiographic assessments

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