Patient Digital Twins for Dynamic Hospital Supply Chain Management AI-Based Predictive Resource Allocation

Abstract

The fusion of Digital Twin (DT) technology and Artificial Intelligence (AI) holds great promise for the optimization of hospital supply chain management and resource allocation. This paper proposes a patient-specific digital twin framework aimed at forecasting hospital staffing needs and aiding supply planning by means of AI-based analytics. Preprocessing and modeling of hospital records that included admission information, medical procedures, room types, usage of supplies, and patterns of staffing were achieved by utilizing advanced machine learning algorithms. The strategy illustrated better performance compared to baseline strategies and had interpretability from feature importance analysis, which emphasized length of stay, critical care admissions, and specialized procedures as influential drivers of staffing requirements. The results show that the patient digital twin can optimize operational efficiency, avoid supply deficits, and facilitate evidence-based decisions. The suggested framework is consistent with the vision of contemporary healthcare supply chains in that it promotes resilience, flexibility, and smart resource management.

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