The healthcare supply chain involves obtaining resources, managing supplies, and distributing products and services to patients across different teams, stakeholders, and regions. However, due to its complex nature, the healthcare supply chain is vulnerable to fraud, inaccurate information, and lack of transparency. To address these challenges, a new approach called the Legitimacy Backpropagation-based Neural Termite Blockchain Framework (LBbNTBF) was developed. This model aims to ensure the security of medications by improving product traceability. Initially, a supply chain management system was implemented, integrating perceptron neural networks to enhance manufacturing capabilities and optimize storage capacities. Additionally, retailers distribute goods to various partners and facilities such as hospitals and clinics based on current demand and specific needs. Pharmacists are responsible for directly providing medications to patients or end users. Performance indicators such as MSE, RMSE were evaluated and compared against alternative models. The improvement was demonstrated by validating the performance of this method against various existing models. The LBbNTBF approach achieved an R2 score of 98.9%, surpassing the effectiveness of alternative approaches.

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Blockchain Assessment for Medical Supply Chain Management

  • Prasanna Kumar Reddy Gurijala,
  • Sohit Reddy Kalluru,
  • Venkata Obula Reddy Puli,
  • Lohith Reddy Kalluru,
  • Pavana Kumari Gavva

摘要

The healthcare supply chain involves obtaining resources, managing supplies, and distributing products and services to patients across different teams, stakeholders, and regions. However, due to its complex nature, the healthcare supply chain is vulnerable to fraud, inaccurate information, and lack of transparency. To address these challenges, a new approach called the Legitimacy Backpropagation-based Neural Termite Blockchain Framework (LBbNTBF) was developed. This model aims to ensure the security of medications by improving product traceability. Initially, a supply chain management system was implemented, integrating perceptron neural networks to enhance manufacturing capabilities and optimize storage capacities. Additionally, retailers distribute goods to various partners and facilities such as hospitals and clinics based on current demand and specific needs. Pharmacists are responsible for directly providing medications to patients or end users. Performance indicators such as MSE, RMSE were evaluated and compared against alternative models. The improvement was demonstrated by validating the performance of this method against various existing models. The LBbNTBF approach achieved an R2 score of 98.9%, surpassing the effectiveness of alternative approaches.