Design and Performance Analysis of an Efficient Blockchain Framework of Patient-Centric Healthcare Systems

Abstract

The conventional healthcare blockchain is considered one of the most preferred technologies in the current global scenario; however, it has several disadvantages, such as low throughput, high latency, and a resource-intensive nature, which limit its ease of availability and accessibility, particularly in storing and securing the medical data with increasing user count day by day. To overcome the challenges, ScalEdge-Health, a blockchain-edge hybrid scalability framework is proposed in the present work. which unifies dynamic edge AI validation and federated learning and sharding ledger partition to securely, and patient-centrically manage healthcare data. The system consists of Lightweight Edge Blockchain Engine (LEBE) and the Cross-Shard Transaction Orchestrator (CSTO), which are primarily used for on-device diagnosis and efficient multi-shard medical record synchronization, respectively. The Lightweight Edge Blockchain Engine (LEBE) operates in conjunction with quantized CNN–BERT models. Integrated Federated Learning is beneficial as it enables model updates without sharing raw data, thereby supporting privacy-preserving data updates and ensuring compliance with HIPAA and GDPR regulations. Evaluation of the data sets are performed by using MIMIC-III and MIT-BIH datasets It was observed that CloudSim simulations demonstrate near-linear scalability across 100 to 1000 nodes (R² = 0.981). A significant improvement was clearly noted, with throughput increasing by 4.5 times, latency reducing by 33%, and energy consumption decreasing by 41% compared to the traditional single-chain healthcare blockchain. By integrating sharding, adaptive edge computing, and decentralized artificial intelligence, ScalEdge emerges as an energy-aware, highperformance, and scalable architecture for next-generation healthcare systems.