Collaborative Research: Physiologically Based Optimization of ICU Management

重症监护室（ICU）是一种重要且昂贵的资源。通过ICU的患者流量建模具有挑战性，因为它需要患者生理学的随机和动态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）基于患者生理和转移延迟动态来创建动态转移准备得分和患者住院时间（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得分来做出预期的床位请求; 3）通过建立新的“基于分数”的路由和随机网络模型，研究ICU和下游单元之间的路由控制问题。PI将招募代表性不足的本科生和研究生参加这个项目。该项目的智力价值在于将生理学的随机和动态模型与患者流量管理相结合，以改善患者的治疗效果和运营效率。这些任务将需要定制的随机建模和优化技术。任务我将介绍一个基于生理的随机和动态的传输准备分数，考虑生理以及阻塞延迟。任务II将制定一个预期的床位申请计划，以优化患者从ICU到下游单位的过渡以及患者结局。近似技术和最优性界限将开发的问题。任务III将创建基于评分的服务分配和随机网络模型，其中服务分配是外源随机过程（患者生理学）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，其中路由决策是针对治疗中的患者（服务中的工作）而不是队列中的患者。此外，一个分散的网络优化将在上下文中进行研究。这些模型将被用来确定ICU容量的决定，通过两阶段的随机程序，其中追索权问题捕获的底层网络。

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.