Modeling and Evaluation of Clustering Patient Care into Bubbles
Modeling and Evaluation of Clustering Patient Care into Bubbles
复制标题
将患者护理聚类为气泡的建模和评估
DOI：
10.1109/ichi52183.2021.00023
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sriram V. Pemmaraju
中科院分区：
文献类型：
--
作者：
D. M. H. Hasan;Alex Rohwer;Hankyu Jang;Ted Herman;P. Polgreen;Daniel K. Sewell;B. Adhikari;Sriram V. Pemmaraju
COVID-19给世界各地的医疗机构造成了巨大的负担。将患者和医疗保健专业人员（HCPs）聚集到“气泡”中已被提出作为一种感染控制机制。在本文中，我们提出了一种新颖而灵活的模型，用于将医疗保健设施中的患者护理聚集到气泡中，以最大限度地减少感染传播。我们的模型旨在控制患者/居民和HCPs的各种成本，以避免集群式患者护理的隐性下游不良影响。这个模型导致了一个离散的优化问题，我们称之为BUBBLECLUSTERING问题。该问题以一个时间访问图作为输入，该访问图表示HCP的移动性，包括HCP对病人/住院病房的访问。该问题的输出是一个重新布线的访问图，通过将HCP和病房划分为气泡，并将HCP访问重新布线到病房，从而使患者护理主要局限于构建的气泡中。尽管bubbleclu群集问题一般来说是难以解决的，但我们提出了一个整数线性规划（ILP）公式，可以最优地解决典型医院单位和长期护理设施出现的问题实例。我们称我们的整体解决方案为成本意识网络重新布线（CoRN）。我们使用来自医院重症监护病房和两个长期护理设施的细粒度运动数据来评估CoRN。这些数据是通过我们建造和部署的传感器系统获得的。从我们的实验结果中得出的主要结论是，在不牺牲患者护理的情况下，通过将患者和医护人员聚集在一起，使用玉米可以大大减少感染的传播，并且在轮班期间，医护人员在时间和距离方面的额外成本最小。
COVID-19 has caused an enormous burden on healthcare facilities around the world. Cohorting patients and healthcare professionals (HCPs) into “bubbles” has been proposed as an infection-control mechanism. In this paper, we present a novel and flexible model for clustering patient care in healthcare facilities into bubbles in order to minimize infection spread. Our model aims to control a variety of costs to patients/residents and HCPs so as to avoid hidden, downstream adverse effects of clustering patient care. This model leads to a discrete optimization problem that we call the BUBBLECLUSTERING problem. This problem takes as input a temporal visit graph, representing HCP mobility, including visits by HCPs to patient/resident rooms. The output of the problem is a rewired visit graph, obtained by partitioning HCPs and patient rooms into bubbles and rewiring HCP visits to patient rooms so that patient-care is largely confined to the constructed bubbles. Even though the BUBBLECLUSTERING problem is intractable in general, we present an integer linear programming (ILP) formulation of the problem that can be solved optimally for problem instances that arise from typical hospital units and long-term-care facilities. We call our overall solution approach Cost-aware Rewiring of Networks (CoRN). We evaluate CoRN using fine-grained-movement data from a hospital-medical-intensive-care unit as well as two long-term-care facilities. These data were obtained using sensor systems we built and deployed. The main takeaway from our experimental results is that it is possible to use CoRN to substantially reduce infection spread by cohorting patients and HCPs without sacrificing patient-care, and with minimal excess costs to HCPs in terms of time and distances traveled during a shift.
影响因子：
158.5
作者：
Needleman, J;Buerhaus, P;Zelevinsky, K
通讯作者：
Zelevinsky, K