The rising incidence of chronic diseases and the outbreak of infectious diseases have posed significant challenges to regional healthcare systems. As critical institutions within these systems, hospitals urgently need effective monitoring of their status and developing strategies to ensure their continued operation. This study introduces the concept of hospital robustness and investigates the ability of hospitals to maintain normal operations and provide continuous, efficient medical services under conditions of patient surges. Utilizing a cascade model, we construct a patient-sharing network to simulate the patient consultation and referral processes between doctors and explore the impact of different referral strategies on hospital robustness. Firstly, based on the daily new patient numbers, an analytical framework was proposed to classify hospital operational states into three phases (no-loss phase, stable phase, and fluctuating phase). Then, two referral strategies were considered: two edge-weighted and completely random referral strategies. When the edge-weighted referral strategy was implemented with lower values, the total average referral count (TAR) and total number of lost patients (TLP) reached their minimum without referral preferences, which helped reduce patient loss. Under the utterly random referral strategy, referring patients randomly to other doctors alleviated the workload of individual doctors and contributed to reducing patient loss. It was also observed that when the limit on referral times was low, the effectiveness of referral strategies diminished, highlighting the need to encourage multiple referrals.

中文翻译：

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患者共享网络稳健性的级联模型


慢性病发病率的上升和传染病的爆发给区域医疗保健系统带来了重大挑战。作为这些系统中的关键机构，医院迫切需要对其状态进行有效监测并制定策略以确保其持续运营。本研究引入了医院稳健性的概念，并调查了医院在患者激增的情况下维持正常运营和提供持续、高效的医疗服务的能力。利用级联模型，我们构建了一个患者共享网络，以模拟医生之间的患者咨询和转诊过程，并探索不同转诊策略对医院稳健性的影响。首先，根据每日新增患者数量，提出分析框架，将医院运营状态分为三个阶段（无损失阶段、稳定阶段和波动阶段）。然后，考虑了两种推荐策略：两种边缘加权和完全随机的推荐策略。当以较低的值实施边缘加权转诊策略时，总平均转诊人数 （TAR） 和丢失患者总数 （TLP） 在没有转诊偏好的情况下达到最小值，这有助于减少患者流失。在完全随机的转诊策略下，将患者随机转诊给其他医生减轻了个体医生的工作量，有助于减少患者流失。还观察到，当推荐时间限制较低时，推荐策略的有效性会降低，这凸显了鼓励多次推荐的必要性。