Optimal patient allocation for echocardiographic assessments

• URL: http://arxiv.org/abs/2506.06297v1
• Date: Sat, 17 May 2025 17:51:23 GMT
• Title: Optimal patient allocation for echocardiographic assessments
• Authors: Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein, Frederick Damen, Alison L. Marsden, Daniele E. Schiavazzi,
• Abstract summary: We estimate patient no-show probabilities and derive empirical distributions of arrival times and exam durations.<n>We develop a discrete-event simulation model using SimPy and integrate it with the open source Python library.<n>Considering a hospital configuration with a 1:6 ratio of fetal to non-fetal rooms and a 4:2 ratio of fetal to non-fetal sonographers, we show that on-the-fly allocation generally yields better performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scheduling echocardiographic exams in a hospital presents significant challenges due to non-deterministic factors (e.g., patient no-shows, patient arrival times, diverse exam durations, etc.) and asymmetric resource constraints between fetal and non-fetal patient streams. To address these challenges, we first conducted extensive pre-processing on one week of operational data from the Echo Laboratory at Stanford University's Lucile Packard Children's Hospital, to estimate patient no-show probabilities and derive empirical distributions of arrival times and exam durations. Based on these inputs, we developed a discrete-event stochastic simulation model using SimPy, and integrate it with the open source Gymnasium Python library. As a baseline for policy optimization, we developed a comparative framework to evaluate on-the-fly versus reservation-based allocation strategies, in which different proportions of resources are reserved in advance. Considering a hospital configuration with a 1:6 ratio of fetal to non-fetal rooms and a 4:2 ratio of fetal to non-fetal sonographers, we show that on-the-fly allocation generally yields better performance, more effectively adapting to patient variability and resource constraints. Building on this foundation, we apply reinforcement learning (RL) to derive an approximated optimal dynamic allocation policy. This RL-based policy is benchmarked against the best-performing rule-based strategies, allowing us to quantify their differences and provide actionable insights for improving echo lab efficiency through intelligent, data-driven resource management.

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