Complexity Science Hub Vienna, Josefstädter Straße 39, 1080, Vienna, Austria.
Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Spitalgasse 23, 1090, Vienna, Austria.
Nat Commun. 2022 Jul 23;13(1):4259. doi: 10.1038/s41467-022-31766-7.
Patients do not access physicians at random but rather via naturally emerging networks of patient flows between them. As mass quarantines, absences due to sickness, or other shocks thin out these networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, we propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model. For each region and medical specialty we construct patient-sharing networks and stress-test these by removing physicians. This allows us to measure regional resilience indicators describing how many physicians can be removed before patients will not be treated anymore. Our model could therefore enable health authorities to rapidly identify bottlenecks in access to care. Here, we show that regions and medical specialties differ substantially in their resilience and that these systemic differences can be related to indicators for individual physicians by quantifying their risk and benefit to the system.
患者并不是随机选择医生的，而是通过他们之间自然形成的患者流动网络进行选择。随着大规模隔离、病假缺勤或其他冲击使这些网络变得稀疏，系统可能会被推向一个临界点，从而失去提供医疗服务的能力。在这里，我们提出了一个基于数据的框架，通过基于代理的模型来量化区域对这些冲击的弹性。对于每个地区和医疗专业，我们构建患者共享网络，并通过去除医生来对其进行压力测试。这使我们能够衡量描述在多少名医生被移除后患者将无法得到治疗的区域弹性指标。因此，我们的模型可以使卫生当局能够快速识别医疗服务获取方面的瓶颈。在这里，我们表明，不同地区和医疗专业在弹性方面存在显著差异，并且可以通过量化个体医生对系统的风险和收益，将这些系统差异与个体医生的指标联系起来。
