Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare

Area of Science:

• Computer Science
• Information Technology
• Healthcare Informatics

Background:

• Blockchain technology offers enhanced privacy and security for enterprise applications.
• Scalability remains a significant challenge for blockchain in large organizations, particularly in healthcare with its vast data requirements.
• Existing blockchain models and consensus algorithms face limitations in complex hierarchical structures.

Purpose of the Study:

• To propose and evaluate a hierarchical blockchain model within Hyperledger Fabric for the healthcare sector.
• To address the scalability and performance issues of traditional database management systems in medical centers.
• To demonstrate the effectiveness of a multi-level organizational structure in a blockchain network.

Main Methods:

• Implementation of a hierarchical model in Hyperledger Fabric, featuring multiple organizational levels (hospitals, governance, insurance).
• Initial deployment with two hierarchy levels, simulating hospitals joining an insurance network.
• Performance evaluation using Hyperledger Caliper, by scaling factors like organizations, transactions, channels, block intervals, and block sizes.

Main Results:

• The hierarchical model's performance was tested and analyzed under various scaling conditions.
• Key performance indicators such as success/failure rates, throughput, and latency were measured.
• The study focused on assessing scalability using patient data within the proposed healthcare blockchain framework.

Conclusions:

• The proposed hierarchical model shows potential for improving blockchain scalability in healthcare applications.
• This approach can mitigate performance bottlenecks associated with large-scale data management in medical centers.
• Further research and testing are warranted to fully explore the model's capabilities and optimize its implementation.