Artificial intelligence-driven improvement of hospital logistics management resilience: a practical exploration based on H Hospital
arXiv:2603.13816v1 Announce Type: new Abstract: Hospital logistics management faces growing pressure from internal operations and external emergencies, with artificial intelligence (AI) holding untapped potential to boost its resilience. This study explores AI's role in enhancing logistics resilience via a mixed-methods case study of H Hospital, combining 12 key informant interviews and a full survey of 151 logistics staff, with the PDCA cycle as the analytical framework. Thematic and quantitative analyses (hierarchical regression, structural equation modeling) were adopted for data analysis. Results showed 94.7% staff perceived AI application, with the strongest improvements in equipment maintenance (41.1%) and resource allocation (33.1%), but limited effects in emergency response (18.54%) and risk management (15.23%). AI integration positively correlated with logistics resilience (\b{eta}=0.642, p<0.001), with management system adaptability as a positive moderator (\b{eta}=0.208, p<
arXiv:2603.13816v1 Announce Type: new Abstract: Hospital logistics management faces growing pressure from internal operations and external emergencies, with artificial intelligence (AI) holding untapped potential to boost its resilience. This study explores AI's role in enhancing logistics resilience via a mixed-methods case study of H Hospital, combining 12 key informant interviews and a full survey of 151 logistics staff, with the PDCA cycle as the analytical framework. Thematic and quantitative analyses (hierarchical regression, structural equation modeling) were adopted for data analysis. Results showed 94.7% staff perceived AI application, with the strongest improvements in equipment maintenance (41.1%) and resource allocation (33.1%), but limited effects in emergency response (18.54%) and risk management (15.23%). AI integration positively correlated with logistics resilience (\b{eta}=0.642, p<0.001), with management system adaptability as a positive moderator (\b{eta}=0.208, p<0.01). The PDCA cycle fully mediated the AI-resilience relationship. We conclude AI effectively enhances logistics resilience, dependent on adaptive management systems and structured continuous improvement mechanisms. Targeted strategies are proposed to form an AI-driven closed-loop resilience mechanism, offering empirical guidance for AI-hospital logistics integration and resilient health system construction.
Executive Summary
This article presents a case study on the effectiveness of artificial intelligence (AI) in enhancing hospital logistics management resilience. Conducted at H Hospital, the study employed a mixed-methods approach combining qualitative and quantitative data. The results indicate that AI integration significantly improved logistics resilience, with notable enhancements in equipment maintenance and resource allocation. However, limited effects were observed in emergency response and risk management. The study also highlights the importance of adaptive management systems and structured continuous improvement mechanisms in maximizing AI-driven resilience. Targeted strategies are proposed to establish an AI-driven closed-loop resilience mechanism. This research offers valuable insights for AI-hospital logistics integration and resilient health system construction.
Key Points
- ▸ The study employed a mixed-methods approach combining qualitative and quantitative data.
- ▸ AI integration significantly improved logistics resilience at H Hospital.
- ▸ Notable enhancements were observed in equipment maintenance and resource allocation, but limited effects in emergency response and risk management.
Merits
Strength of Methodology
The mixed-methods approach allows for a comprehensive understanding of AI's impact on logistics resilience, combining qualitative insights with quantitative data.
Practical Implications
The study provides actionable strategies for integrating AI into hospital logistics management, enabling healthcare institutions to enhance their resilience.
Demerits
Limited Generalizability
The study's focus on a single hospital (H Hospital) may limit its generalizability to other healthcare institutions, requiring further research to validate the findings.
Methodological Limitations
The reliance on key informant interviews and surveys may introduce biases and limitations, particularly if respondents are not representative of the broader logistics staff population.
Expert Commentary
This study makes a significant contribution to the field of healthcare logistics, providing empirical evidence on the effectiveness of AI in enhancing resilience. While the study's focus on a single hospital may limit its generalizability, the findings offer valuable insights for healthcare institutions seeking to integrate AI into their logistics management systems. The study's emphasis on adaptive management systems and structured continuous improvement mechanisms is particularly noteworthy, highlighting the importance of organizational adaptability in maximizing AI-driven resilience. As the healthcare sector continues to grapple with the challenges of internal and external emergencies, this research offers timely guidance on the potential for AI to enhance resilience and improve operational efficiency.
Recommendations
- ✓ Future research should focus on validating the study's findings in other healthcare institutions to ensure generalizability and identify best practices for AI-driven logistics resilience.
- ✓ Healthcare policymakers should develop targeted initiatives to support AI adoption in logistics management, recognizing the potential for AI-driven resilience to improve patient outcomes and reduce healthcare costs.