Boodaghian Asl Arsineh, Raghothama Jayanth, Darwich Adam S, Meijer Sebastiaan
Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, 14157, Sweden.
Sci Rep. 2025 Apr 8;15(1):12052. doi: 10.1038/s41598-025-96536-z.
Hospitals are complex systems, and the flow of patients is dynamic and nonlinear in such systems. Network representation allows flow algorithms to observe bottlenecks as candidates for optimisation. To model the dynamic behaviour of the patient flow, we need to consider the variability in arrival rates and service times (length of stay). Previously proposed dynamic flow algorithms mainly focused on arrival and departure rates, inflow and outflow, edges' and vertices' capacity, and routing, with applications mainly in transportation and telecommunication. In hospitals, bottlenecks that emerge from the patients' flow are a result of the vertices (wards) behaviour defined by capacity (beds), number of servers (staff), service time variability, and edges (care pathways) distribution probability. We offer a modified flow algorithm that takes a hospital network, iterates over the patients' arrival rates, and measures the flow with respect to vertices' capacities, servers, service time variability, edge capacity, and distribution probability. The result is a dynamic residual graph to measure the bottlenecks' persistency and severity, identify the root causes of bottlenecks, and wards' dynamic nonlinear behaviour. The algorithm provides a quick holistic view of hospital performance and the analysis of the edges and vertices' behaviour over time.
医院是复杂的系统，在这样的系统中患者流是动态且非线性的。网络表示法使流量算法能够将瓶颈视为优化的候选对象。为了对患者流的动态行为进行建模，我们需要考虑到达率和服务时间（住院时长）的变异性。先前提出的动态流量算法主要关注到达率和离开率、流入和流出、边和顶点的容量以及路由，主要应用于交通运输和电信领域。在医院中，患者流中出现的瓶颈是由容量（床位）、服务器数量（工作人员）、服务时间变异性所定义的顶点（病房）行为以及边（护理路径）分布概率的结果。我们提供一种改进的流量算法，该算法以医院网络为基础，遍历患者的到达率，并根据顶点容量、服务器、服务时间变异性、边容量和分布概率来测量流量。结果是一个动态残差图，用于测量瓶颈的持续性和严重性，识别瓶颈的根本原因以及病房的动态非线性行为。该算法提供了医院绩效的快速整体视图以及对边和顶点随时间变化行为的分析。
