Artificial intelligence-driven improvement of hospital logistics management resilience: a practical exploration based on H Hospital

2026-03-14

AI Summary

This study addresses the challenge of insufficient resilience in hospital logistics management under both routine operations and emergency scenarios. Focusing on Hospital H as a case, the research integrates in-depth interviews with key informants and a high-response-rate questionnaire survey among logistics staff (94.7% valid return rate). Building upon the PDCA (Plan–Do–Check–Act) cycle, the authors develop an analytical framework and employ a mixed-methods approach—including thematic analysis, hierarchical regression, and structural equation modeling—to empirically demonstrate, for the first time, that artificial intelligence (AI) significantly enhances logistics resilience through a full mediation effect via the PDCA cycle (β = 0.642, p < 0.001). Management adaptability is identified as a critical moderating factor. The study proposes an AI-driven closed-loop resilience mechanism, with practical implementation showing marked improvements in equipment maintenance (+41.1%) and resource allocation (+33.1%).

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• hospital logistics management
• resilience
• artificial intelligence
• emergency response
• risk management

Innovation

• Artificial Intelligence
• Logistics Resilience
• PDCA Cycle
• Healthcare Management
• Structural Equation Modeling