ItemConference Paper

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

Title
Patient Digital Twins for Dynamic Hospital Supply Chain Management AI-Based Predictive Resource Allocation
Creator
Avhad, Priyanka Ravindra; Radhakrishnan, G.V.; Parashar, Jyoti; Malik, Khusbhoo; Gupta, Komal; Upreti, Kamal
Description
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. 2025 IEEE.
Source
Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Artificial Intelligence; Digital Twin; Hospital Supply Chain; Predictive Analytics; SHAP Interpretability; Staffing Optimization
Coverage
Avhad P.R., Dept. of Computer Science& Engineering, Veermata Jijabai Technological Institute, Mumbai, India; Radhakrishnan G.V., Department of Economics & Finance, Kalinga School of Management KIIT, Bhubaneswar, India; Parashar J., Department of Computer Applications, Panipat Institute of Engineering & Technology College, Haryana, Panipat, India; Malik K., School of Law, Christ University Delhi NCR, Ghaziabad, India; Gupta K., Accenture, Bangalore, India; Upreti K., Department of Computer Science, Christ University Delhi NCR, Ghaziabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155641-9;
Format
online
Language
English
Type
Conference paper

Citation

Avhad, Priyanka Ravindra; Radhakrishnan, G.V.; Parashar, Jyoti; Malik, Khusbhoo; Gupta, Komal; Upreti, Kamal, “Patient Digital Twins for Dynamic Hospital Supply Chain Management AI-Based Predictive Resource Allocation,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25957.

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