A Brief Bibliometric Analysis and Visualisation of Scopus and WoS databases on Blockchain Technology in Healthcare Domain

Background: The aim of this study is to analyse the work carried out in healthcare or medical domain using blockchain technology for privacy and security of patient’s data, their healthcare records. The documents published in Scopus and Web of Science databases during the year 2016 to present (February 2021) have been considered for survey. Methods: Scopus and Web of Science(WoS), most popular databases are used to retrieve documents which were published between years 2016 to present. Scopus analyser and web of Science analyser are used for analysis of various parameters such as documents published per year, sources of documents, number of citations and so on. VOSviewer1.6.16 software tool is used for analysis of different units such as citations, co- authorship etc. Results: During our survey we have retrieved a total 598 documents related to blockchain technology in the healthcare domain which are published from year 2016 on wards from scopus database. Using a web of science database 594 documents has been retrieved for the same domain. Statistical analysis and network analysis shows that there is tremendous growth in publications from year 2019 and 2020 on blockchain technology. The United States, India and China are major contributors. Conclusions: Databases are analysed in terms of number of documents per year, sources of publications, authors correlation, documents per country, funding agencies etc parameters are statistically analysed. Using statistical and network analysis we can conclude that there is huge scope to work in the blockchain domain to achieve more privacy, security, and data integrity

Enhancing Public Healthcare Security: Integrating Cutting-Edge Technologies into Social Medical Systems

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities and potential threats in the IoT-23 dataset. The results contribute to the wider discussion on enhancing the security of public healthcare systems. They provide suggestions for incorporating anomaly detection, encryption protocols, and continuous monitoring to strengthen the security of Social Medical Systems. This research provides guidance for policymakers, healthcare practitioners, and technologists as they navigate the changing landscape of healthcare digitization. It advocates for the proactive integration of advanced technologies to ensure the security, privacy, and accessibility of healthcare information within the interconnected web of Social Medical Systems. DOI: https://doi.org/10.52710/seejph.48

Enhanced Information Systems Success Model for Patient Information Assurance

The current health information systems have many challenges such as lack of standard user interfaces, data security and privacy issues, inability to uniquely identify patients across multiple hospital information systems, probable misuse of patient data, high technological costs, resistance to technology deployments in hospital management, lack of data gathering, processing and analysis standardization. All these challenges, among others hamper either the acceptance of the health information systems, operational efficiency or expose patient information to cyber attacks. In this paper, an enhanced information systems success model for patient information assurance is developed using an amalgamation of Technology Acceptance Model (TAM) and Information Systems Success Model (ISS). This involved the usage of Linear Structured Relationship (LISREL) software to model a combination of ISS and Intention to Use (ITU), TAM and ITU, ISS and user satisfaction (US), and finally TAM and US. The sample size of 110 respondents was obtained based on the total population of 221 using the Conhrans formula. Thereafter, simple random sampling was employed to select members within each category of employees to take part in the study. The questionnaire as a research tool was checked for reliability via Cronbach’s Alpha. The results obtained showed that for ISS and ITU modeling, only perceived ease of use, system features, response time, flexibility, timeliness, accuracy, responsiveness and user training positively influenced the intention to use. However, for the TAM and ITU modeling, only TAM’s measures such as timely information, efficiency, increased transparency, and proper patient identification had a positive effect on intension to use. The ISS and US modeling revealed that perceived ease of use had the greatest impact on user satisfaction while response time had the least effect on user satisfaction. On its part, the TAM and US modeling showed that timely information, effectiveness, consistency, enhanced communication, and proper patients identification had a positive influence on user satisfaction

The Effectiveness of Medical Record Software to Improve Administrative Service Ability and Student Motivation

Technology can improve the learning process more quickly and effectively to improve students' cognitive skills. Technology-based learning media is not only for prospective teachers but can be helpful for prospective health workers. One indicator that shows prospective health workers' quality is the health services' ability. Therefore, preparing prospective health worker students who are technologically literate and highly motivated is necessary. This study aims to determine and analyze the effectiveness of Medical Information System software to improve the ability of health administration services and student motivation. The quantitative method with pre-experimental design. Pre-experimental design with one group through pre-test, intervention (treatment), and post-test. This research was conducted at STIKes Cirebon with a total sample of 110 students. The results showed that after implementing the medical record software, descriptively, there was an increase in the percentages of responsiveness, reliability, assurance, empathy, and administrative service quality. In addition, each indicator of student motivation increases. Based on hypothesis testing, it can be concluded that Medical Information System medical record software can significantly improve the ability of health administration services and student motivation. This research contributes to providing information to students and lecturers about health administration learning technology

Access to Opioids in Palliative Care in Low-and Middle-Income Countries : The Case of Burkina-Faso -How Can Blockchain and Internet of Things Assist? –

Background: People requesting palliative care or suffering from pain are subjected to adhere to opioid-based treatment in order to alleviate their pain. Commonly, access to opioids is strictly controlled. Access to Healthcare delivery services remains challenging in Low-and Middle-Income Countries (LMIC). In Burkina-Faso (BF), a Sub-Saharan African (SSA) country, patients requiring palliative care (PC) are especially facing poor access to pain drugs such as morphine. Facing poor access to pain-alleviating medicine can severely impact the daily quality of life (QoL). On one hand, patients are experiencing poor opioids access. On another hand opioids abuse, leading to drug addiction is noticed. The question arising here is how can they face poor access and at the same time abuse the given drug? One plausible answer is the counterfeit chain, which provides illegal drugs. Beyond the counterfeit issues faced, the public health care system is also facing, amongst others, prescription falsification, fraud in the distribution, and stock shortage. Method & Design:  Mixed-Method-Design was applied to this study. National stipulations, regulations, and the state-of-the-art in the field of palliative care in BF were investigated and analyzed. Based on the investigation‘s outcomes and following the paradigm of design science research, and information system based improvement solution is proposed to tackle the poor access to opioids, improve the supply and distribution chain as well as to efficiently monitor the consumption of opioids in BF, and prevent patients from any health issues, drug addiction, and death. Objectives: The main objectives are to fight against opioid addiction, counterfeits, a stock shortage, and prevent related health safety issues. The main aim is to enable the traceability of any opioids prescription, secure the supply and distribution, and thus early detect any fraud in the system. This editorial paper would, therefore, focus on investigating the reasons underlying the poor access to opioids in palliative care in BF and make suggestions for improvement. A blockchain (BC) and the Internet of Things (IoT) based system to secure and improve opioids supply, distribution, and prescription will be proposed. Results: The contribution analysis reveals the potential of the proposed model to assist in many ways to improve access to opioids and to secure this access. The model could contribute to preventing drug abuse, overprescription, supporting off-label-use of opioids and thus providing a knowledge database for off-label use of opioids. This model shows promise to deliver accurate data and information about the exact opioid’s needs and consumption atlas. This will assist to better distribute the product in the entire country. A proof-of-concept of the proposed model is required. This is ongoing and will be presented in a forthcoming paper. Conclusion: This editorial paper investigates access to opioids in Burkina Faso. It pointed out by analyzing out the computer science perspectives the different causes of the crisis. A contextualized model is provided. A test in situ needs to be performed

Detecting Sybil Attack in Blockchain and Preventing through Universal Unique Identifier in Health Care Sector for privacy preservation

Health care data requires data secrecy, confidentiality, and distribution through public networks. Blockchain is the latest and most secure framework through which health care data can be transferred on the public network. Blockchain has gained attention in recent year’s due to its decentralized, distributed, and immutable ledger framework. However, Blockchain is also susceptible to many attacks in the permission less network, one such attack is known as Sybil attack, where several malicious nodes are created by the single node and gain multiple undue advantages over the network. In this research work, the Blockchain network is created using the smart contract method which gets hampered due to Sybil attack. Thus, a novel method is proposed to prevent Sybil attack in the network for privacy preservation. Universal Unique Identifier code is used for identification and prevention of the Sybil attack in the self-created networks. Results depict that proposed method correctly identifies the chances of attack and the prevention from the attack. The approach has been evaluated on performance metrics namely, true positive rate and accuracy which were attained as 87.5 % and 91% respectively, in the small network. This demonstrates that the proposed work attains improved results as compared to other latest available methods

A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors

Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and privacy protection issues of collected personal neurophysiological data. Therefore, in this article, we try to bridge this gap and propose a secure privacy-preserving protocol for implementing BCI applications. We first transformed brain signals into images and used generative adversarial network to generate synthetic signals to protect data privacy. Subsequently, we applied the paradigm of transfer learning for signal classification. The proposed method was evaluated by a case study and results indicate that real electroencephalogram data augmented with artificially generated samples provide superior classification performance. In addition, we proposed a blockchain-based scheme and developed a prototype on Ethereum, which aims to make storing, querying and sharing personal neurophysiological data and analysis reports secure and privacy-aware. The rights of three main transaction bodies - construction workers, BCI service providers and project managers - are described and the advantages of the proposed system are discussed. We believe this paper provides a well-rounded solution to safeguard private data against cyber-attacks, level the playing field for BCI application developers, and to the end improve professional well-being in the industry

Blockchain for global vaccinations efforts: State of the art, challenges, and future directions

The emergence of the coronavirus disease 2019 (COVID-19) global crisis negatively affected all aspects of human life. One of the most important methods used worldwide to survive this global crisis is the vaccination process to circumvent the proliferation of this pandemic. Many restrictions were alleviated in many countries such as access to public facilities and events. There is a huge amount of data about vaccination campaigns that are collected and maintained worldwide. Although the vaccination data can be analyzed to find out how the alleviation of restrictions can be applied if the data management process requires preserving key aspects like trust, transparency, and availability for easy and reliable access to such data. In this regard, blockchain technology is an excellent choice for meeting the requirements and providing a secure trusted framework for global verification. In this article, the related literature on blockchain technology is surveyed and summarized for all systems that embody solutions. The pros and cons of each solution are presented and provide a comparative summary. Furthermore, a detailed analysis is given to present the current problems and provide a promising mechanism to verify the vaccinated persons anywhere in the world, in a secure manner while retaining individual privacy

Dwarna : a blockchain solution for dynamic consent in biobanking

Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed.peer-reviewe