Efficient patient-centric EMR sharing block tree

TL;DR

This work tackles the scalability and patient-centricity challenges of blockchain-based EMR sharing. It introduces MedBlockTree, a growing block-tree structure that uses an identity-based chameleon hash to permit collision blocks and parallel consensus across multiple branches, paired with EnhancedPro, a PoS/BFT protocol enabling multiple winners per round. The design preserves patient awareness through collision blocks tied to patients' keys and reduces on-chain bottlenecks by processing several blocks in a single consensus round while keeping raw data off-chain. Simulations demonstrate substantial throughput gains across branch counts, latency, network size, and collision rates, highlighting the approach's potential for scalable, secure healthcare data sharing, albeit with diminishing returns as the tree expands.

Abstract

Flexible sharing of electronic medical records (EMRs) is an urgent need in healthcare, as fragmented storage creates EMR management complexity for both practitioners and patients. Blockchain has emerged as a promising solution to address the limitations of centralized EMR systems regarding interoperability, data ownership, and trust concerns. Whilst its healthcare implementation continues to face scalability challenges, particularly in uploading lag time as EMR volumes increase. In this paper, we describe the design of a novel blockchain-based data structure, MedBlockTree, which aims to solve the scalability issue in blockchain-based EMR systems, particularly low block throughput and patient awareness. MedBlockTree leverages a chameleon hash function to generate collision blocks for existing patients and expand a single chain into a growing block tree with $n$ branches that are capable of processing $n$ new blocks in a single consensus round. We also introduce the EnhancedPro consensus algorithm to manage multiple branches and maintain network consistency. Our comprehensive simulation evaluates performance across four dimensions: branch number, worker number, collision rate, and network latency. Comparative analysis against a traditional blockchain-based EMR system demonstrates outstanding throughput improvements across all dimensions, achieving processing speeds $ν\cdot n$ times faster than conventional approaches.

Figures (12)

• Figure 1: Block structure
• Figure 2: Alice's former and collision block
• Figure 3: Example of the default chain B1
• Figure 4: Example of MedBlockTree with two branches
• Figure 5: Pre-vote and Commit vote