Collaborative Research: Physiologically Based Optimization of ICU Management

重症监护病房(ICU)是一种重要而昂贵的资源。对通过ICU的患者流量进行建模是具有挑战性的，因为它需要一个随机和动态的患者生理模型。在该奖项的支持下，研究人员将使用详细的数据集来建立一个随机和动态的患者生理模型，目的是改善ICU出院预测和随后通过医院的流量。这是通过三个任务来实现的：1)基于患者生理和转运延迟动态建立动态转运准备分数和病人住院时间(LOS)的随机模型；2)开发优化模型以使用所创建的动态和随机分数进行预期床位请求；3)通过建立新的基于分数的排队和随机网络模型来研究ICU与下游单元之间的路径控制问题。私人投资促进机构将招收人数不足的本科生和研究生参加这一项目。该项目的智能优点在于将随机和动态生理学模型与患者流管理相结合，以改善患者结果和操作效率。这些任务将需要定制随机建模和优化技术。任务I将引入一个基于生理的随机和动态传输就绪分数，该分数既考虑了生理延迟也考虑了阻塞延迟。任务二将制定一个预期的床位要求方案，以优化患者从ICU到下游病房的过渡以及患者的结果。将为该问题开发近似技术和最优界。任务III将创建基于分数的排队和随机网络模型，其中服务分布是外部随机过程(患者生理)的函数，从而捕捉到ICU中LOS的高度变异性。将开发基于分数的路径控制策略和算法，其中为正在接受治疗的患者(服务中的工作)而不是队列中的患者做出路径决定。此外，还将在此背景下研究去中心化网络优化。这些模型将被用来通过一个两阶段随机规划来确定ICU的容量决策，在该规划中，追索权问题捕获了基本的排队网络。

An Intensive Care Unit (ICU) is an important and expensive resource. Modeling patient flow through an ICU is challenging because it requires a stochastic and dynamic model of patient physiology Under the auspices of this award, the investigators will use a detailed data set to build a stochastic and dynamic model of patient physiology with the objective of improving ICU discharge predictions and subsequent flow through the hospital. This is achieved through three tasks: 1) creating a dynamic Transfer Readiness Score and a stochastic model of patient length of stay (LOS) based on patient physiology and transfer delay dynamics; 2) developing an optimization model to make anticipative bed requests using the created dynamic and stochastic score; 3) investigating the routing control problems between the ICUs and downstream units by developing new "score-based" queueing and stochastic network models. The PIs will recruit under-represented undergraduate and graduate students to this project. The intellectual merit of this project lies in the integration of stochastic and dynamic models of physiology with patient flow management to improve patient outcomes as well as operational efficiency. These tasks will require customization of stochastic modeling and optimization techniques. Task I will introduce a physiologically based stochastic and dynamic transfer readiness score that considers physiology as well as blocking delays. Task II will develop an anticipative bed request scheme to optimize patient transitions from ICUs to the downstream units as well as patient outcomes. Approximation techniques and optimality bounds will be developed for the problem. Task III will create score-based queueing and stochastic network models in which the service distribution is a function of an exogenous stochastic process (patient physiology), thus capturing the high variability of LOS in the ICUs. Score-based routing control policies and algorithms will be developed, where routing decisions are made for patients in treatment (jobs in service) rather than in queue. In addition, a decentralized network optimization will be studied in the context. These models will be used to determine ICU capacity decisions through a two-stage stochastic program, in which the recourse problem captures the underlying queueing network.