Healthcare Security Research Articles

Role of Blockchain Technology in Data Security for Healthcare

Role of Blockchain Technology in Data Security for Healthcare

Data-driven exploration of terbium-doped tungsten oxide for Ultra-Precise detection of 3H-2B: Implications for gas sensor applications

Gas sensing materials based on semiconducting metal oxides (SMOs) have gained attention in various fields such as air quality control, environmental monitoring, healthcare, and defense security. To overcome challenges in conventional research, a predictive approach using data-driven models and a comprehensive dataset of 150,000 articles is proposed. This approach evaluates the optimal combination of terbium-doped tungsten oxide as a sensing material. The adverse effects of doping elements on the sensing performance of tungsten oxide are identified, and strategies for optimization are provided. A nanocarbon sphere composite doped with terbium-doped oxide is synthesized and exhibits exceptional sensitivity and selectivity towards volatile organic compounds (VOC) gases, particularly 3-hydroxy-2-butanone (3H-2B). The gas sensor's performance is enhanced by minimizing the loss function of the probability distribution. Functionalized tungsten oxide demonstrates outstanding sensitivity and selectivity towards 3H-2B gas, even in high-interference environments, with a low detection limit of 20 parts per billion (ppb). A multimodal construction approach is used to predict, evaluate, and analyze the performance of sensing materials. This approach enables real-time transmission of sensor test data to cloud platforms, providing an efficient research methodology for the development and design of next-generation functional materials.

The Influence of School Heads’ Motivation Strategies on Teachers’ Performance in Public Secondary Schools in Arusha Region, Tanzania

This study investigated the influence of school heads’ motivation strategies on teachers’ performance in public secondary schools in Arusha Region, Tanzania. Objectives were to; assess the status of teachers’ performance in public secondary schools and determine the influence of school heads’ motivation strategies on teachers’ performance in public secondary schools in Arusha Region. Motivation management and Walberg’s achievement theories guided the study. Mixed methods approach and concurrent triangulation research design were adopted. Questionnaires and an interview guide were used to collect data from 88 teachers and 8 school heads. Stratified and simple random sampling techniques were employed to select teachers while school heads were purposively sampled. Validity was established through expert judgment. The reliability index, TQ; r=0.78for questionnaire was obtained using Split Half method. Quantitative data were analyzed using descriptive statistics and inferentially using Pearson’s Product Moment Correlation Analysis in the SPSS version 23. Results were presented in tables. Qualitative data were thematically analyzed alongside research questions and presented in narrative forms. Findings established that many teachers were not able to cover syllabus in time which occasioned low CSEE performance, partly attributed to inadequate motivation strategies adopted by school heads. In conclusion, any form of motivation plays an important role in improving teachers’ timely syllabus coverage and improved students’ academic performance. The study recommends that school heads should create new strategies for motivating teaching staff to boost their morale. Education stakeholders should provide funds to provide conducive working environment through comfortable accommodation, accessible healthcare services and security.

Assessing occupational pesticide exposure in Latin America and the Caribbean: Methods review for agricultural vulnerability reduction

B A rapid review was conducted to describe pesticide exposure assessment methodologies in Agricultural Work Contexts (AWC)s in Latin America and the Caribbean (LAC), in order to provide elements to reduce vulnerabilities within the agricultural population. A final number of 79 articles were selected from Medline via Pubmed, LILACS, and SciELO. In the region, family agriculture is the predominant form of agricultural production (72%) in informal agricultural work contexts (AWCs), which are characterized by small-scale diversified agricultural production, lower educational attainment, limited income, and a lack of social and healthcare security for the families. Indirect exposure assessment methods were the most commonly reported (58%), and its use was associated with informal AWCs (p = 0.011). Understanding AWCs and employing appropriate assessment methods can contribute to addressing vulnerabilities in the agricultural sector.

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

Drug Traceability - Divine or Challenge for Pharma Sector

Blockchain technology is driving the digital transformation of various industries, and one sector benefiting from this innovation is the pharmaceutical industry. This industry grapples with challenges like a lack of transparency, difficulties in tracking products, a deficit of trust, and issues related to shipping expired products. To address these concerns, blockchain technology has been harnessed as a solution. Notably, counterfeit drug prevention emerged as the most prevalent category, aligning with the pharmaceutical industry's primary objective. Blockchain technology is an emerging innovation that is finding applications in various industries, including healthcare. In the healthcare sector, Blockchain networks are being utilized to securely store and exchange patient data across hospitals, diagnostic laboratories, pharmacies, and medical practitioners. These applications of Blockchain can effectively identify and mitigate critical errors, including potentially hazardous ones within the realm of healthcare. Consequently, this technology holds the promise of enhancing the efficiency, security, and transparency of medical data sharing within the healthcare system. Moreover, it offers valuable tools for medical institutions to gain insights and improve the analysis of medical records. It visually represents the diverse capabilities, enablers, and the unified workflow process of Blockchain technology in supporting healthcare on a global scale. Additionally, the paper presents a thorough discussion of fourteen significant applications of Blockchain in healthcare, underscoring its pivotal role in addressing issues like deception in clinical trials. Blockchain's potential in healthcare lies in improving data efficiency, assuaging concerns about data manipulation, and establishing a robust data storage framework with the highest level of security. It delivers features such as versatility, interconnectivity, accountability, and authentication for data access. The protection and confidentiality of health records are of paramount importance, and Blockchain facilitates decentralized data security in healthcare while mitigating specific threats.

Return to work policies and practices after total hip or knee arthroplasty in Denmark, Germany, and the Netherlands: an exploratory study

Purpose To explore return-to-work (RTW) policies and practices for total hip arthroplasty (THA) and total knee arthroplasty (TKA) patients in three European countries. Materials and methods An exploratory study in Denmark, Germany, and the Netherlands consisting of three aspects: (1) description of the healthcare and social security systems; (2) identification of national RTW guidelines; (3) a survey to gain insight into RTW practices and perceptions of orthopaedic surgeons, including barriers, facilitators, and needs. Results Healthcare and social security systems differed (e.g. fast-track vs longer postoperative stay; coverage of rehabilitation costs). National guidelines were available in Germany (THA, TKA) and the Netherlands (TKA), containing limited RTW information. The survey was completed by 168 orthopaedic surgeons (Denmark n = 51; Germany n = 39; the Netherlands n = 78). Overall, orthopaedic surgeons reported being in need of more knowledge and better collaboration with other healthcare practitioners. Conclusion We found considerable variation in healthcare and social security systems. When available, national guidelines contained limited information. In all three countries surgeons need more knowledge and better collaboration with other healthcare practitioners. We advise that RTW multidisciplinary recommendations post THA/TKA be established by the national associations of the healthcare practitioners involved.

Improving patient centric data retrieval and cyber security in healthcare: privacy preserving solutions for a secure future

Improving patient centric data retrieval and cyber security in healthcare: privacy preserving solutions for a secure future

PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber-Physical Systems.

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber-physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient's Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle's Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient's gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient's BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time.

An architectural framework of elderly healthcare monitoring and tracking through wearable sensor technologies

The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments.

Big Data Security on Hadoop Open Source Frame for Healthcare Data Management using One-Time-Pad Encryption Algorithm

The study elicited knowledge about the factors associated with one-time pad encryption/decryption with big data in healthcare; formulate an assembled algorithms model for one-time pad encryption; design and implement the system and evaluating the system performance with the view implementing big data security on Hadoop open-source framework for healthcare data. Literature was sourced to investigate the factors associated with healthcare security attacks and various consequences of breach of data. An assembled algorithm model was formulated using mathematical theory of one-time pad encryption and a model was designed using Universal Modelling Language (UML) and implemented using python programming language, Distributed File System of Hadoop, Yet Another Resource Negotiator called YARN; encryption time and decryption time was adopted for the performance metrics deployed for the evaluation of the developed system. The result showed that as the size of the files increased, the encryption/decryption time keeps increasing as well. While carryout the algorithm evaluation, two different values (file sizes) were used for testing on the Hadoop framework.Securing the healthcare (Ebola) big-data, it was observed that OTP encryption/decryption performed better compared to AES encryption/decryption in term of computational processing time of the healthcare big-data considered. Considering before/after downloading, it was observed that there was need for authentication for another level of security towards securing healthcare records on HDFS. The study concluded that, big data analytics on Hadoop is ideal for today’s big healthcare data and also that One Time Pad encryption algorithm is sufficient to provide needed big healthcare data security.

Secure cloud storage for IoT based distributed healthcare environment using blockchain orchestrated and deep learning model

Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods.

Knowledge, Attitude, Means and Infrastructure and BPJS (Healthcare and Social Security Agency) Patient Referral

Data derived from the Mundu Community Health Center Unit showed that the achievement of referrals was 2,419 (16%) which exceeded the target of the BPJS referral standard (15%). Primary health facilities that refer patients excessively will result in the accumulation of patients in hospitals and will have an impact on a the declining quality of health services . This study aims to determine the relationship between knowledge, attitudes, facilities and BPJS patient referral. This was a quantitative study with cross sectional design. The population was BPJS patients as many as 15,005 people and the samples were 99 people collected with accidental sampling method. Data were obtained through interviews and documentation studies. The study instrument used here was s a questionnaire. Data analysis used chi square test. Based on the results of hypothesis test, it was found that there was a relationship between knowledge, attitude , infrastructure facilities and BPJS patient referral.

Secure internet of medical things based electronic health records scheme in trust decentralized loop federated learning consensus blockchain

Electronic Health Records (EHRs) have become an increasingly significant source of information for healthcare professionals and researchers. Two technical challenges are addressed: motivating federated learning members to contribute their time and effort, and ensuring accurate aggregation of the global model by the centralized federated learning server. To overcome these issues and establish a decentralized solution, the integration of blockchain and federated learning proves effective, offering enhanced security and privacy for smart healthcare. The proposed approach includes a gamified element to incentivize and recognize contributions from federated learning members. This research work offers a solution involving resource management within the Internet of Medical Things (IoMT) using a newly proposed trust decentralized loop federated learning consensus blockchain. The obtained raw data is pre-processed by using handling missing values and adaptive min-max normalization. The appropriate features are selected with the aid of hybrid weighted-leader exponential distribution optimization algorithm. Because, data with multiple features exhibits varying levels of variation across each feature. The selected features are then forwarded to the training phase through the proposed pyramid squeeze attention generative adversarial networks to classify the EHR as positive and negative. The proposed classification model demonstrates high flexibility and scalability, making it applicable to a wide range of network architectures for various computer vision tasks. The introduced model provides better outcomes in terms of 98.5% in the training accuracy and 99% in the validation accuracy over Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which is more efficient than the other traditional methods.

Identification of Water Crisis Management Policy Indicators in the Country Based on the Interpretive Structural Modeling (ISM) Approach

Objective: The aim of the present research was practical in terms of its objectives and descriptive-survey in terms of its data collection method, utilizing an Interpretive Structural Modeling (ISM) approach. Methodology: The participants of this study were 15 managers, policymakers, and water sector experts, who were purposefully invited to participate in this research. The tool used in this investigation was a closed-response questionnaire in a matrix format. The criterion for expertise at this stage was the respondent's professional legitimacy. Findings: The results of implementing the research model indicate that the most important components of the study include legal and regulatory environment factors, economic environment, cultural environment, conflict of interest management, culture building, participation drivers, trust, political strategies, crisis management necessity, constructive institutional interaction, motivation, communication factors, governmental actions, reform and adaptation, facilitative leadership, decision-making stability, financial support, guarantee of sustainable development, control of marginalization, attention to local capacities, appropriate water pricing, promotion of public monitoring, improvement of agricultural water use efficiency, consumption pattern improvement, and legal support. Conclusion: Therefore, issues such as economy and budgeting, healthcare, welfare and social security, education, energy, environmental damage, foreign policy, and national internal security, as well as threats to occupational and job security, or matters like insufficient access to healthcare services should be examined by the government, the private sector, and the commitment of individuals and companies, or a combination of both.

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning.

Privacy Enhancing Technologies (PETs) have the potential to enable collaborative analytics without compromising privacy. This is extremely important for collaborative analytics can allow us to really extract value from the large amounts of data that are collected in domains such as healthcare, finance, and national security, among others. In order to foster innovation and move PETs from the research labs to actual deployment, the U.S. and U.K. governments partnered together in 2021 to propose the PETs prize challenge asking for privacy-enhancing solutions for two of the biggest problems facing us today: financial crime prevention and pandemic response. This article presents the Rutgers ScarletPets privacy-preserving federated learning approach to identify anomalous financial transactions in a payment network system (PNS). This approach utilizes a two-step anomaly detection methodology to solve the problem. In the first step, features are mined based on account-level data and labels, and then a privacy-preserving encoding scheme is used to augment these features to the data held by the PNS. In the second step, the PNS learns a highly accurate classifier from the augmented data. Our proposed approach has two major advantages: 1) there is no noteworthy drop in accuracy between the federated and the centralized setting, and 2) our approach is flexible since the PNS can keep improving its model and features to build a better classifier without imposing any additional computational or privacy burden on the banks. Notably, our solution won the first prize in the US for its privacy, utility, efficiency, and flexibility.

РАЗВИТИЕ СИСТЕМЫ ДОЛГОВРЕМЕННОГО УХОДА: ДЕМОГРАФИЧЕСКИЕ ФАКТОРЫ И СОЦИАЛЬНО-ЭКОНОМИЧЕСКИЕ ПРЕДПОСЫЛКИ

The subject of the study is the long-term care system in terms of providing healthcare and social security services for pensioners and disabled people. The purpose of the paper is to analyze and assess the demographic and socio-economic factors that influence the formation and development of the long-term care system in Russia. The relevance of the study stems from the rising proportion of the population above working age and specific aspects of their socio-economic status. The scientific novelty lies in the new approaches to building and advancing the long-term care system, emphasizing the need for it to be based on regional and territorial disparities in the provision of social and medical assistance to pensioners and disabled individuals, as well as the demographic characteristics of regional development. The study utilized factor analysis, generalization, grouping, comparison, and extrapolation, along with other methods, to identify the potential need for long-term care. This considered demographic factors and aimed to justify the introduction of long-term care systems with state support. The analysis also focused on socioeconomic factors and emphasized the aspects of formation and development of long-term care, while taking into account the territorial and regional specifics of Russia. The study aimed to provide scientific and practical results. The authors conclude that it is necessary to consider the existing features of territorial and regional development that do not provide equal access to health and social care services and the socio-economic situation of target categories of citizens when forming and developing a comprehensive long-term care system in Russia.

Perceptions and attitudes towards unmanned aerial vehicles (drones) use for delivery of HIV medication among fisher folk communities on the Islands of Kalangala, Uganda.

The study aimed to assess the attitudes of stakeholders towards the use of unmanned aerial vehicles (medical drones) for delivering antiretroviral therapy (ART) in the Kalangala district of Uganda, which is comprised of 84 islands and has approximately 18,500 People Living with HIV (PLHIV). A qualitative baseline study was conducted to assess the acceptability and feasibility of using a medical drone for ART delivery in the island settlements of Kalangala Islands. The data revealed four emerging themes: knowledge about the drones, perceived benefits of medical drone delivery, perceived risks of medical drone use, and recommendations for future use. The study found that most participants, especially healthcare workers and key opinion leaders, were aware of the medical drones, which could reduce transport costs, deliver medication on time, and reduce healthcare workers' workload. However, there were also perceived risks related to the use of medical drones, such as stigma, reduced contact with healthcare providers, and maintenance and security issues. The study provided evidence that medical drones would be acceptable and have support from various stakeholders in the island settlements for ART delivery. However, concerns were raised about potential stigma and less health worker interaction. This qualitative work allowed the team to address these concerns during the pilot phase.

Secure Electronics Medical Infrastructure for Healthcare 4.0: A Voice Identity Management-Based Approach

Securing Electronic Medical Records for Healthcare 4.0 is one of the prime requirements in the medical care service domains. Healthcare 4.0 refers to the evolution process of healthcare systems that leverage digital technologies, connectivity, and data-driven insights to provide more personalized and efficient care. With the increasing adoption of electronic healthcare records and the growing importance of data in the healthcare sector, ensuring the privacy of patient data becomes paramount. To ensuring the security for healthcare 4.0 in the current work a voice authentication-based system is devised. This enables the protection of electronic healthcare records (EHRs) in the association of healthcare 4.0. More precisely here the voice authentication is used as an additional layer of security for accessing and managing EHRs. The Voice authentication can also be categorized as the voice biometric. This is a technology that uses unique voice characteristics and patterns to verify the identity of individuals. The result establishes the innovative smart security arrangement for the Healthcare 4.0 using the voice authentication-based identity management infrastructure; and a modified version of asymmetric cryptosystem has been used inside the blockchain. The simulation outcome indicates better performance in the form of block generation time, block number, ensure the transaction, and minimize the latency of the transaction.

Secure Health Care Data Transaction Using Hybrid Attribute-Based Encryption with Access Control Policy on Cloud

Secure Health Care Data Transaction Using Hybrid Attribute-Based Encryption with Access Control Policy on Cloud