An Integrated Blockchain with a Hybrid Deep Learning Framework for Enhanced Resource Efficiency in the Supply Chain Healthcare Industry

Abstract

Over the years, fast development has occurred in the healthcare industry, and major challenges healthcare experts and stakeholders face are supply chain management. With an excessive growth in the need for healthcare services and the requirement for effective, cost-effective, and higher-quality healthcare delivery, Healthcare Supply Chain Management (HSCM) became a key aspect in considering success in healthcare systems. In recent times, Blockchain (BC), Artificial Intelligence (AI), and the Internet of Things (IoT) have shown certain possibilities to revolutionize HSCM. The HSCM consists of expiration, counterfeits, product recalls, and monitoring of product supply shortages. BC, incorporated with IoT, is a new technology that can provide a practical solution to the SCM in healthcare. In this manuscript, a Blockchain-Integrated Hybrid Deep Learning model for Supply Chain Management and Resource Efficiency (BCHDL-SCMRE) model is presented in the Healthcare Industry. The paper aims to develop an intelligent and secure framework using advanced techniques to improve transparency, efficiency, and trust in SCM within the healthcare industry. Initially, the BC technology is applied in healthcare supply chains to ensure transparency and security. Next, the Z-score normalization is used in the data pre-processing phase to normalize the input data. To select optimal features, the feature selection process is executed by the Modified Rain Optimization (MRO) algorithm. Furthermore, the hybrid of a Temporal Convolutional Network and Gated Recurrent Unit (TCN-GRU) technique has been deployed for classification purposes. At last, the improved Sparrow Search Algorithm (SSA) is applied for parameter tuning to guarantee that the optimal hyperparameters are picked for improved precision. To display the heightened performance of the proposed BCHDL-SCMRE system, a complete performance assessment is conducted. The comparative outcomes informed the improvised features of the BCHDL-SCMRE method.