Integrating Blockchain and Deep Learning for Enhanced Supply Chain Management in Healthcare: A Novel Approach for Alzheimer’s and Parkinson’s Disease Prevention and Control - Jitender Tanwar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The integration of advanced technologies such as blockchain and deep learning holds significant promise for revolutionizing supply chain management within the healthcare sector. This paper proposes a novel framework that combines these technologies to enhance the efficiency and security of healthcare supply chains, with a specific focus on Alzheimer’s and Parkinson’s disease prevention and control. By leveraging the decentralized and immutable nature of blockchain, the framework ensures data integrity and traceability. Simultaneously, deep learning algorithms provide robust predictive analytics for early disease detection. The paper begins with a thorough review of the existing literature on blockchain applications, deep learning methodologies, and current supply chain management practices in healthcare. This review informs the development of a conceptual model that integrates these technologies into a unified system. The framework is then validated through two case studies focused on Alzheimer’s and Parkinson’s diseases. In these case studies, blockchain technology secures patient data and facilitates seamless information flow, while deep learning models analyze patient data to predict disease onset and progression. The case study results illustrate the effectiveness of this integrated approach in enhancing data security, operational efficiency, and predictive accuracy. The paper further discusses the broader implications of this framework for healthcare supply chain management and its potential impact on disease prevention and control. The findings underscore the potential of integrating blockchain and deep learning to create more secure, efficient, and predictive healthcare systems.