Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a model to reduce recovery unit congestion. We estimate LOS using a variety of machine learning models, schedule procedures with a variety of online optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process. These results highlight the perils of using oversimplified distributional models of patient length of stay for scheduling procedures and the importance of using stochastic optimization well-suited to dealing with long-tailed behavior.
翻译:使用长期和高度可变的外科手术患者住院时间的数据,我们开发了一个减少康复单位拥塞的模型;我们利用各种机器学习模型、各种在线优化模型的时间安排程序以及模拟性能来估计远程医疗;尽管可以使用一套非常丰富的患者特征,但机器学习模型只实现了有限的远程医疗预测准确性;与医院目前使用的纸质系统相比,大多数优化模型未能减少拥塞而不增加手术的等待时间。一个具有足够取样的保守的随机优化,以捕捉远程医疗分发的长尾部,超过了目前的人工流程。这些结果凸显了在时间安排程序上使用过简化的病人逗留时间分配模型的风险,以及使用适合处理长尾行为的随机优化方法的重要性。