A cascade model for the robustness of patient-sharing networks

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.