BACKGROUND: Since 2020, Lebanon has faced a succession of financial, health, and security crises that have severely weakened its hospital system. In this context, marked by armed conflicts and recurrent disasters, the resilience of healthcare facilities has emerged as a major public health and human security concern. This study aims to assess the resilience of Lebanese hospitals in the context of armed conflict by identifying implemented strategies, encountered challenges, and potential avenues for improvement. METHODS: A quantitative, observational, and analytical design was adopted. A structured questionnaire was administered to 412 healthcare professionals working in hospitals located in Beirut and South Lebanon. Data were analyzed using SPSS software, employing descriptive statistics, the Chi-square test of association, and Pearson's correlation coefficient to examine relationships among the studied variables. A multiple linear regression analysis was performed to identify independent predictors of hospital resilience while controlling for the effects of the studied variables. RESULTS: Significant positive correlations were observed between hospital resilience and crisis management protocols (r = 0.377, p < 0.001), cooperation with NGOs and international organizations (r = 0.567, p < 0.001), integration of artificial intelligence (r = 0.340, p < 0.001), and logistical and security challenges (r = 0.389, p < 0.001). In multivariate analysis, crisis management protocols (B = 0.163, p < 0.001), cooperation with NGOs (B = 0.257, p < 0.001), and logistical and security challenges (B = 0.214, p < 0.001) remained significant predictors, while artificial intelligence was not (p = 0.143). CONCLUSION: Hospital resilience is primarily driven by institutional preparedness and interorganizational cooperation. While artificial intelligence shows potential, its independent contribution remains limited. The association between logistical and security challenges and resilience likely reflects adaptive responses under adverse conditions rather than a direct beneficial effect.
Khattar et al. (Tue,) studied this question.