Improving Patient flow in Acute Care

改善急症护理中的患者流动

• 批准号: RGPIN-2015-03911
• 负责人: Patrick, Jonathan
• 金额: $ 1.6万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678727
• 关键词: Improving; Patient; flow; Acute; Care

***In dealing with health care managerial problems, we are often forced to compartmentalize the health system in order to simplify the challenges into a tractable model. This compartmentalization fails to take into account the complexity involved in patient flow through acute care. The success of approximate dynamic programming (ADP) in solving large scale sequential decision problems means that some of that compartmentalization is no longer necessary. The first objective of this research is to merge some of the traditional compartments in OR models in health in order to include the highly dependent nature of these decisions. We begin with a model that incorporates the advanced scheduling and appointment scheduling problems. These problems have traditionally been dealt with in isolation but are in truth highly dependent with the input of the appointment scheduling problem being the output from an advanced scheduling model and the performance of the advanced scheduling model depending on the form of the appointment schedule. We will then increase the complexity of this problem in two ways: first by considering multiple resources consumed in sequence (with specific reference to surgical scheduling where patients consume operating room time followed by recovery time in a ward) and second by incorporating capacity decisions. However even this involves significant compartmentalization as the surgical schedule impacts not only surgical patients but also other patients admitted through the emergency department as both types of patients compete for the same beds. We will thus look to build predictive models that can predict the impact on hospital congestion of implementing the policies derived from the ADP models for surgical scheduling. Finally, the impact of these models is significantly hampered by any delays in discharging patients from the hospital due to a lack of capacity in the community. We will thus continue to explore the capacity planning models for the network of community services that ensure timely discharge from the hospital. Thus our proposal has a three pronged objective:***1) Build a model that combines the advanced scheduling and appointment scheduling problems into one model and that can handle multiple resources consumed in sequence as well as capacity planning decisions.***2) Build a model to predict congestion within a hospital and help assess the output of the model from objective 1.***3) Build a capacity planning model for community care services (queuing theory and optimization) in order to maintain proper flow out of acute care.***Thus, the proposed research will develop a series of interconnected models that together allow a health authority to determine the necessary capacity along the entire care pathway from entry into acute care through to discharge to community services while also providing intelligent scheduling policies that ensure the timeliness of patient service.**

*在处理卫生保健管理问题时，我们经常被迫将卫生系统划分开来，以便将挑战简化为一个易于处理的模型。这种划分没有考虑到急诊病人流动所涉及的复杂性。近似动态规划(ADP)在解决大规模序列决策问题中的成功意味着，一些划分不再是必要的。这项研究的第一个目标是合并健康中OR模型中的一些传统划分，以便包括这些决策的高度依赖性质。我们从一个结合了高级日程安排和预约日程安排问题的模型开始。这些问题传统上是孤立地处理的，但实际上是高度依赖的，预约调度问题的输入是高级调度模型的输出，高级调度模型的性能取决于预约调度的形式。然后，我们将通过两种方式增加这个问题的复杂性：第一，考虑按顺序消耗的多个资源(具体参考手术日程安排，其中患者消耗手术室时间，然后是病房的恢复时间)；第二，合并容量决策。然而，即使这样也涉及到严重的区隔，因为手术时间表不仅影响外科患者，而且影响通过急诊科入院的其他患者，因为这两类患者争夺同一床位。因此，我们将寻求建立预测性模型，以预测实施从ADP手术日程安排模型得出的政策对医院拥堵的影响。最后，这些模式的影响因社区缺乏能力而延误患者出院而受到严重阻碍。因此，我们将继续探索社区服务网络的能力规划模式，以确保及时出院。因此，我们的建议有一个三管齐下的目标：*1)建立一个模型，该模型将高级调度和预约调度问题结合到一个模型中，并且可以处理顺序消耗的多个资源以及容量规划决策。*2)建立一个模型来预测医院内的拥堵，并帮助评估目标1的模型的输出。*3)建立社区护理服务的容量规划模型(排队理论和优化)，以便维持急诊护理的适当流动。*因此，拟议的研究将开发一系列相互关联的模型，共同允许卫生当局确定从进入急性护理到出院到社区服务的整个护理路径的必要容量，同时还提供确保患者服务及时性的智能调度政策。**