Rest Klaus-Dieter, Hirsch Patrick
Institute of Production and Logistics, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180 Vienna, Austria.
Cent Eur J Oper Res. 2022;30(1):133-157. doi: 10.1007/s10100-021-00770-5. Epub 2021 Aug 2.
Home health care (HHC) services are of vital importance for the health care system of many countries. Further increases in their demand must be expected and with it grows the need to sustain these services in times of disasters. Existing risk assessment tools and guides support HHC service providers to secure their services. However, they do not provide insights on interdependencies of complex systems like HHC. Causal-Loop-Diagrams (CLDs) are generated to visualize the impacts of epidemics, blackouts, heatwaves, and floods on the HHC system. CLDs help to understand the system design as well as cascading effects. Additionally, they simplify the process of identifying points of action in order to mitigate the impacts of disasters. In a case study, the course of the COVID-19 pandemic and its effects on HHC in Austria in spring 2020 are shown. A decision support system (DSS) to support the daily scheduling of HHC nurses is presented and applied to numerically analyze the impacts of the COVID-19 pandemic, using real-world data from a HHC service provider in Vienna. The DSS is based on a Tabu Search metaheuristic that specifically aims to deal with the peculiarities of urban regions. Various transport modes are considered, including time-dependent public transport.
居家医疗保健（HHC）服务对许多国家的医疗保健系统至关重要。预计其需求将进一步增加，因此在灾难时期维持这些服务的需求也随之增长。现有的风险评估工具和指南支持HHC服务提供商保障其服务。然而，它们并未提供关于HHC等复杂系统相互依存关系的见解。生成因果循环图（CLD）以可视化流行病、停电、热浪和洪水对HHC系统的影响。CLD有助于理解系统设计以及连锁效应。此外，它们简化了识别行动点的过程，以减轻灾难的影响。在一个案例研究中，展示了2020年春季奥地利COVID-19大流行的过程及其对HHC的影响。提出了一个支持HHC护士日常排班的决策支持系统（DSS），并使用来自维也纳一家HHC服务提供商的真实数据对其进行数值分析，以分析COVID-19大流行的影响。该DSS基于一种禁忌搜索元启发式算法，专门旨在应对城市地区的特殊性。考虑了各种交通方式，包括与时间相关的公共交通。
