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。在该奖项的支持下，研究人员将使用详细的数据集来建立患者生理学的随机和动态模型，目的是改善ICU出院预测和随后的医院流量。这是通过三个任务来实现的：1)创建一个动态的转移准备评分和基于患者生理和转移延迟动态的患者住院时间（LOS）随机模型；2)建立优化模型，利用创建的动态和随机评分来预测床位需求；3)通过建立新的“基于分数”的排队和随机网络模型，研究icu与下游单元之间的路由控制问题。该项目将招收代表性不足的本科生和研究生。该项目的智力优势在于将随机和动态生理学模型与患者流程管理相结合，以改善患者预后和操作效率。这些任务将需要定制随机建模和优化技术。任务一将介绍一个基于生理学的随机和动态转移准备度评分，它考虑了生理学和阻塞延迟。任务二将制定一个预期的床位请求方案，以优化患者从icu到下游单位的过渡以及患者的结果。逼近技术和最优界将发展为问题。任务III将创建基于分数的排队和随机网络模型，其中服务分布是外生随机过程（患者生理）的函数，从而捕获icu中LOS的高度可变性。将开发基于分数的路由控制策略和算法，其中为正在治疗的患者（在职工作）而不是排队的患者做出路由决策。此外，将在此背景下研究分散的网络优化。这些模型将用于通过两阶段随机规划确定ICU容量决策，其中追索权问题捕获底层排队网络。

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.