SeFra: A Secure Framework to Manage eHealth Records Using Blockchain Technology
01 Jan 2020-International Journal of E-health and Medical Communications (IGI Global)-Vol. 11, Iss: 1, pp 1-16
TL;DR: The proposed work provides a secure framework to manage the eHealth record by using blockchain (SeFra), where a temporal shadow is used and the integrity of health records is ensured by blockchain technology.
Abstract: Electronic health information is an efficient technique for providing health care services to society. Patient health information is stored in the cloud, to allow access of eHealth information from anywhere, and at any time, but the technical problems are security, privacy, etc. Sharing the medical data in a trustless environment is overcome by the proposed framework SeFra. The proposed work provides a secure framework to manage the eHealth record by using blockchain (SeFra). For authentication purposes, a temporal shadow is used and the integrity of health records is ensured by blockchain technology.
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TL;DR: The first systematic review on blockchain-based personal health records (PHRs) is presented in this paper, where the authors examine the current landscape, design choices, limitations, and future directions of blockchainbased PHRs, and reveal that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage.
Abstract: Background: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing.
Objective: This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs.
Methods: Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form.
Results: A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage.
Conclusions: This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space.
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TL;DR: In this paper , the authors focused on data pertinent to diabetic retinopathy disease and its prediction, and used the SqueezeNet classifier to predict the occurrence of diabetic Retinopathy (DR) disease.
Abstract: Blockchain technology has gained immense momentum in the present era of information and digitalization and is likely to gain extreme popularity among the next generation, with diversified applications that spread far beyond cryptocurrencies and bitcoin. The application of blockchain technology is prominently observed in various spheres of social life, such as government administration, industries, healthcare, finance, and various other domains. In healthcare, the role of blockchain technology can be visualized in data-sharing, allowing users to choose specific data and control data access based on user type, which are extremely important for the maintenance of Electronic Health Records (EHRs). Machine learning and blockchain are two distinct technical fields: machine learning deals with data analysis and prediction, whereas blockchain emphasizes maintaining data security. The amalgamation of these two concepts can achieve prediction results from authentic datasets without compromising integrity. Such predictions have the additional advantage of enhanced trust in comparison to the application of machine learning algorithms alone. In this paper, we focused on data pertinent to diabetic retinopathy disease and its prediction. Diabetic retinopathy is a chronic disease caused by diabetes and leads to complete blindness. The disease requires early diagnosis to reduce the chances of vision loss. The dataset used is a publicly available dataset collected from the IEEE data port. The data were pre-processed using the median filtering technique and lesion segmentation was performed on the image data. These data were further subjected to the Taylor African Vulture Optimization (AVO) algorithm for hyper-parameter tuning, and then the most significant features were fed into the SqueezeNet classifier, which predicted the occurrence of diabetic retinopathy (DR) disease. The final output was saved in the blockchain architecture, which was accessed by the EHR manager, ensuring authorized access to the prediction results and related patient information. The results of the classifier were compared with those of earlier research, which demonstrated that the proposed model is superior to other models when measured by the following metrics: accuracy (94.2%), sensitivity (94.8%), and specificity (93.4%).
4 citations
TL;DR: This paper focuses on ensuring the integrity of the health record with context-based Merkle tree (CBMT) through temporal shadow with general public ledger (GPL) and personalized micro ledger (PML).
Abstract: The patient's health record is sensitive and confidential information. The sharing of health information is a first venture to make health services more productive and improve the quality of healthcare services. Decentralized online ledgers with blockchain-based platforms were already proposed and in use to address the interoperability and privacy issues. However, other challenges remain, in particular, scalability, usability, and accessibility as core technical challenges. The paper focuses on ensuring the integrity of the health record with context-based Merkle tree (CBMT) through temporal shadow. In this system, two ledgers were used to ensure the integrity of eHealth records like general public ledger (GPL) and personalized micro ledger (PML). The context-based Merkle tree (CBMT) is used to aggregates all the transactions at a particular time. The context means it depends on time, location, and identity. This is ensured without the help of a third party.
1 citations