Secure E-Healthcare System Using Blockchain Technology: A Review

2021 ◽  
Vol 8 (4) ◽  
Keyword(s):  
Healthcare System ◽  
Blockchain Technology

scholarly journals MedShard: Electronic Health Record Sharing Using Blockchain Sharding

2021 ◽  
Vol 13 (11) ◽  
pp. 5889
Author(s):  
Faiza Hashim ◽  
Khaled Shuaib ◽  
Farag Sallabi
Keyword(s):  
Healthcare System ◽  
Small Groups ◽  
Network Performance ◽  
Communication Overhead ◽  
Privacy And Security ◽  
Health Records ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Simulation Results ◽  
Electronic Health

Electronic health records (EHRs) are important assets of the healthcare system and should be shared among medical practitioners to improve the accuracy and efficiency of diagnosis. Blockchain technology has been investigated and adopted in healthcare as a solution for EHR sharing while preserving privacy and security. Blockchain can revolutionize the healthcare system by providing a decentralized, distributed, immutable, and secure architecture. However, scalability has always been a bottleneck in blockchain networks due to the consensus mechanism and ledger replication to all network participants. Sharding helps address this issue by artificially partitioning the network into small groups termed shards and processing transactions parallelly while running consensus within each shard with a subset of blockchain nodes. Although this technique helps resolve issues related to scalability, cross-shard communication overhead can degrade network performance. This study proposes a transaction-based sharding technique wherein shards are formed on the basis of a patient’s previously visited health entities. Simulation results show that the proposed technique outperforms standard-based healthcare blockchain techniques in terms of the number of appointments processed, consensus latency, and throughput. The proposed technique eliminates cross-shard communication by forming complete shards based on “the need to participate” nodes per patient.

scholarly journals Blockchain Technology: Investing in a National EMR Strategy

2019 ◽  
Vol 87 (2) ◽  
pp. 12-14
Author(s):  
Josiah Marquis ◽  
Erik Elliott ◽  
Martyn Dahal ◽  
Dor Abelman
Keyword(s):  
Healthcare System ◽  
Information Transfer ◽  
Stakeholder Collaboration ◽  
Health Technologies ◽  
Universal Healthcare ◽  
Blockchain Technology ◽  
Quality Of Medical Care ◽  
Unique Framework ◽  
Universal Healthcare System

Improving the efficiency and sustainability of Canada’s healthcare system is currently being prioritized by the federal government. In order to achieve this, government and non-government stakeholder collaboration will be required to improve integration, universality, and equity for all populations throughout the country. Although technological innovations pose certain risks, investing in health technologies plays a major role in improving service delivery and cost-saving within healthcare. Canada has historically had a relatively low use of electronic medical records (EMR’s) in comparison to other high-income countries - which appears to be partially due to inefficiencies within the fragmented systems throughout Canada. Improving the current EMR infrastructure has the potential to save our country substantial amounts of money, improve information transfer for patients and practitioners, and enhance the overall quality of medical care Canadian citizens receive. There has been mention of developing a national EMR strategy by various organizations, including the Canadian Medical Association. Blockchain technology appears to have many desired characteristics for developing a comprehensive national EMR strategy to support the needs of our universal healthcare system. This is due to the fact that it is a secure platform to store huge amounts of information and make data transfers between a diverse group of stakeholders. It is believed that blockchain can provide a unique framework upon which a national data system can be built.

Building an IPFS and Blockchain-Based Decentralized Storage Model for Medical Imaging

2021 ◽  
pp. 19-40
Author(s):  
Randhir Kumar ◽  
Rakesh Tripathi
Keyword(s):  
Medical Imaging ◽  
Healthcare System ◽  
Medical Image ◽  
Healthcare Systems ◽  
Third Party ◽  
Storage Model ◽  
Image Sharing ◽  
Blockchain Technology ◽  
Proposed Model ◽  
Real Time Application

Currently, sharing and access of medical imaging is a significant element of present healthcare systems, but the existing infrastructure of medical image sharing depends on third-party approval. In this chapter, the authors have proposed a framework in order to provide a decentralized storage model for medical image sharing through IPFS and blockchain technology that remove the hurdle of third-party dependency. In the proposed model, the authors are sharing the imaging and communications in medicine (DICOM) medical images, which consist of various information related to disease, and hence, the framework can be utilized in the real-time application of the healthcare system. Moreover, the framework maintains the feature of immutability, privacy, and availability of information owing to the blockchain-based decentralized storage model. Furthermore, the authors have also discussed how the information can be accessed by the peers in the blockchain network with the help of consensus. To implement the framework, they have used the python ask and anaconda python.

scholarly journals Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Reyes-González Juan Pablo ◽  
Díaz-Peregrino Roberto ◽  
Soto-Ulloa Victor ◽  
Galvan-Remigio Isabel ◽  
Castillo Paul ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Healthcare System ◽  
Medical Doctors ◽  
Blockchain Technology ◽  
System A ◽  
Healthcare Professions ◽  
State Of Art ◽  
Digital Anatomy

Abstract In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies. Thus, we are explaining the basis of these systems focused to the medical arena in order to encourage medical doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy system.

Using Blockchain Technology, Artificial and Natural Neural Network in Bioinformatics

2021 ◽  
pp. 743-750
Author(s):  
Lubov A. Belyanina
Keyword(s):  
Neural Network ◽  
Electronic Health Records ◽  
Healthcare System ◽  
Health Sector ◽  
Security And Privacy ◽  
Health Records ◽  
Healthcare Data ◽  
Skin Cells ◽  
Blockchain Technology ◽  
Diagnosis Method

Blockchain technology has come up with solutions for the issues faced in the current healthcare system. Stakeholders and researchers can share electronic health records to find the solution to diseases. This not only improves the health sector, but it also provides information about other diseases. To maintain the security and privacy of the patient, a healthcare data gateway storage platform was created based on the blockchain technique. To overcome the problem, this article proposes a dengue diagnosis method based on nucleotides in the gene sequence, it needs only skin cells, hair, or a nail which can be collected easily from the patients. The proposed method not only diagnoses dengue, but also classifies serotypes using wavelet coefficients of EIIP indicator sequences.

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