TY - JOUR
T1 - Demand-Shock Detection for Hospital Supply Chains: A Resilience Analytics Blueprint for Critical Consumables
AU - George Brown
AU - Michael Taylor
AU - Sarah Wilson
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2025
VL - 5
IS - 2
SP - 50
N2 - Hospital supply chains face unprecedented vulnerabilities from demand shocks, such as pandemics or natural disasters, which disrupt the availability of critical consumables like personal protective equipment and medications. This conceptual manuscript proposes a resilience analytics blueprint leveraging artificial intelligence (AI) to detect and mitigate these shocks in healthcare systems. Drawing on clinical AI architectures, healthcare analytics infrastructures, and electronic health record (EHR) intelligence ecosystems, we introduce the demand-shock adaptive resilience network (DSARN), a novel framework for proactive monitoring and orchestration. DSARN integrates decision support pipelines with AI governance mechanisms to enable real-time anomaly detection without empirical data or model training. Key components include layered interoperability frameworks for data exchange across hospital nodes and workflow integration models that prioritize critical consumables. Conceptual formulas illustrate risk propagation through supply networks and governance load on monitoring systems. By synthesizing recent literature on AI deployment in healthcare, this blueprint emphasizes theoretical infrastructures for enhancing supply chain resilience, addressing interoperability challenges, and ensuring ethical governance. The architecture fosters adaptive feedback topologies to anticipate disruptions, offering a pathway for hospitals to build robust analytics ecosystems. Ultimately, DSARN provides a theoretical foundation for transforming reactive supply management into predictive resilience, safeguarding patient care amid volatility.
UR - https://cirpublications.com/i451277708
ER -