Collaborative Research: Physiologically Based Optimization of ICU Management

合作研究：基于生理的ICU管理优化

• 批准号: 1635410
• 负责人: Guodong Pang
• 金额: $ 15万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1635410&HistoricalAwards=false
• 关键词: Collaborative; Research; Physiologically; Based; Optimization

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.

重症监护病房 (ICU) 是一种重要且昂贵的资源。对通过 ICU 的患者流程进行建模具有挑战性，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构建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程。这是通过三项任务实现的：1) 根据患者生理学和转移延迟动态创建动态转移准备评分和患者住院时间 (LOS) 随机模型； 2) 开发优化模型，使用创建的动态和随机评分提出预期的床位请求； 3）通过开发新的“基于分数”的排队和随机网络模型来研究 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与该项目。该项目的智力优势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随机建模和优化技术。任务我将介绍一个基于生理学的随机和动态转移准备评分，该评分考虑了生理学以及阻塞延迟。任务 II 将制定预期床位申请方案，以优化患者从 ICU 到下游病房的过渡以及患者的治疗结果。将为该问题开发近似技术和最优边界。任务 III 将创建基于评分的排队和随机网络模型，其中服务分配是外生随机过程（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，其中路由决策是为治疗中的患者（服务中的工作）而不是排队的患者做出的。此外，还将在此背景下研究去中心化网络优化。这些模型将用于通过两阶段随机程序确定 ICU 容量决策，其中追索问题捕获底层排队网络。