Efficient patient-centric EMR sharing block tree

• URL: http://arxiv.org/abs/2504.20544v1
• Date: Tue, 29 Apr 2025 08:42:01 GMT
• Title: Efficient patient-centric EMR sharing block tree
• Authors: Xiaohan Hu, Jyoti Sahni, Colin R. Simpson, Normalia Samian, Winston K. G. Seah,
• Abstract summary: We describe the design of a novel blockchain-based data structure, MedBlockTree.<n>We introduce the EnhancedPro consensus algorithm to manage multiple branches and maintain network consistency.<n>Our comprehensive simulation evaluates performance across four dimensions: branch number, worker number, collision rate, and network latency.
• Score: 2.1833640209032823
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 $\nu\cdot n$ times faster than conventional approaches.

Related papers

• Efficient and Trustworthy Block Propagation for Blockchain-enabled Mobile Embodied AI Networks: A Graph Resfusion Approach [60.80257080226662]
  We propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs.<n>Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model.<n>By leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory.
  arXiv  Detail & Related papers  (2025-01-26T07:47:05Z)
• Dynamic Digital Twins of Blockchain Systems: State Extraction and Mirroring [3.5376671181893897]
  This paper constitutes an effort to design a Digital Twin-based blockchain management framework.<n>It aims to adapt the consensus process to fit the conditions of the underlying system.<n>Specifically, this work addresses the problems of extracting the blockchain system and mirroring it in its digital twin.
  arXiv  Detail & Related papers  (2024-12-07T03:54:34Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• Cross-chain Sharing of Personal Health Records: Heterogeneous and Interoperable Blockchains [1.6662631877618401]
  This paper proposes a scheme for sharing PHRs across heterogeneous and interoperable blockchains. Medical IoT devices can encrypt and store real-time PHRs in an InterPlanetary File System. An enhanced proxy re-encryption(PRE) algorithm addresses the differences in blockchain cryptosystems.
  arXiv  Detail & Related papers  (2024-10-11T12:22:10Z)
• Generative Blockchain: Transforming Blockchain from Transaction Recording to Transaction Generation through Proof-of-Merit [5.801684954657074]
  generative blockchain aims to transform conventional blockchain technology by combining transaction generation and recording. Central to our design is a novel consensus mechanism, Proof-of-Merit (PoM) We demonstrate PoM on a ride service on-demand platform, where the task of solving complex transaction-generating problems is delegated to a pool of independent problem solvers.
  arXiv  Detail & Related papers  (2024-08-23T20:51:10Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• A Scalable Multi-Layered Blockchain Architecture for Enhanced EHR Sharing and Drug Supply Chain Management [3.149883354098941]
  This paper presents a scalable, multi-layered blockchain architecture for secure Electronic Health Record sharing and drug supply chain management.<n>The proposed framework introduces five distinct layers that enhance system performance, security, and patient-centric access control.<n>Our solution ensures data integrity, privacy, and interoperability, making it compatible with existing healthcare systems.
  arXiv  Detail & Related papers  (2024-02-27T09:20:16Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation [93.88170217725805]
  We propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features. Our evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-12-08T18:59:57Z)
• BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare [0.3867363075280543]
  This paper proposes Federated Learning (FL) based smart healthcare system where Medical Centers (MCs) train the local model using the data collected from patients and send the model weights to the miners in a blockchain-based robust framework without sharing raw data, keeping privacy preservation into deliberation. We formulate an optimization problem by maximizing the utility and minimizing the loss function considering energy consumption and FL process delay of MCs for learning effective models on distributed healthcare data underlying a blockchain-based framework.
  arXiv  Detail & Related papers  (2022-07-24T04:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.