Methods and analytical tools to optimize patient flow

优化患者流程的方法和分析工具

• 批准号: RGPIN-2020-07199
• 负责人: Lahrichi, Nadia
• 金额: $ 3.13万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718542
• 关键词: Methods; analytical; tools; optimize; patient

In this proposal, I will focus on applications related to patient flow in the healthcare system. The patient flow problem can be described as follows: Patients arrive in the system one at a time and wait for an appointment. This appointment usually defines the start time of their visit (or the start day of their treatment). One patient may have multiple appointments. Each patient has a profile that is not known in advance. The profile is defined as a set of activities and a duration time for each activity. One or more resources may perform each activity, and some activities have precedence constraints. Each day one or more activities may occur: they may be scheduled one after the other (single appointment) or all at once (multiple appointments). The activities are often related to resources, which must be scheduled to ensure their availability to the patients. Patient delays and bottlenecks are often caused by a lack of coordination of these limited resources. The arrival times, care needs, and service times of the patients are usually unknown. Patient flow problems are difficult to solve. Analysis of the literature shows that: 1) Most research focuses on single-resource scheduling and considers that the demand is known; 2) Uncertainty is poorly integrated and exploited; 3) Patient and resource scheduling problems are often solved sequentially and independently; and 4) Exact approaches have reached their limits when both problems are integrated. While it is not always possible to address these issues simultaneously, my research program aims to work towards addressing the patient flow problem as a whole (for an effective and integrated health system). To address these challenges, my research will be divided into three themes: A) dedicated algorithms for “partially” integrated patient and resource scheduling; B) the search for approaches to more efficient metaheuristics; and C) the search for efficient methods to integrate the solutions of dedicated algorithms. The projects in these areas aim to both tackle practical problems arising in the healthcare system, and to develop innovative methodological approaches that will advance knowledge and contribute to science. One of the objectives of this research is to provide state-of-the-art tools and good practices that can be used by the healthcare industry all over Canada. This research will benefit healthcare decision makers (and therefore the patients) through wait time reduction and facilitated access for patients. It should be able to relieve congestion in clinics and hospitals. My current network of collaborations allows me to have a privileged access to the data, knowledge of the challenges encountered in practice and a validation field for the algorithms. Finally, students that will participate in this research will gain experience in collaborating with industry. They will acquire expertise in the both of research and practice that will be of great assistance in their careers.

在本提案中，我将重点介绍与医疗保健系统中的患者流相关的应用程序。患者流问题可以描述如下：患者一次一个地到达系统并等待预约。该预约通常定义他们就诊的开始时间（或治疗的开始日期）。一个病人可能有多个预约。每个病人都有一个事先不知道的档案。配置文件被定义为一组活动和每个活动的持续时间。一个或多个资源可以执行每个活动，并且某些活动具有优先约束。每一天都可能发生一个或多个活动：它们可以一个接一个地安排（单个预约）或一次全部安排（多个预约）。这些活动通常与资源有关，必须对资源进行安排，以确保患者可以使用这些资源。病人延误和瓶颈往往是由于缺乏协调这些有限的资源。患者的到达时间、护理需求和服务时间通常是未知的。 患者流量问题难以解决。文献分析表明：1）大多数研究集中在单一资源调度，并认为需求是已知的; 2）不确定性是很差的集成和利用; 3）病人和资源调度问题往往是顺序和独立解决的;和4）精确的方法已经达到了极限时，这两个问题的集成。虽然它并不总是能够同时解决这些问题，我的研究计划的目的是努力解决病人流问题作为一个整体（有效的和综合的卫生系统）。 为了应对这些挑战，我的研究将分为三个主题：A）专用算法的“部分”集成的病人和资源调度; B）寻找更有效的元分析方法;和C）寻找有效的方法来整合专用算法的解决方案。 这些领域的项目旨在解决医疗系统中出现的实际问题，并开发创新的方法论方法，以促进知识和科学。这项研究的目标之一是提供最先进的工具和良好的做法，可供加拿大各地的医疗保健行业使用。这项研究将有利于医疗保健决策者（因此患者）通过减少等待时间和方便患者的访问。它应该能够缓解诊所和医院的拥挤情况。我目前的合作网络使我有特权访问数据，了解实践中遇到的挑战和算法的验证领域。最后，将参与这项研究的学生将获得与行业合作的经验。他们将获得研究和实践方面的专业知识，这将对他们的职业生涯有很大的帮助。