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Postgraduate Medicine Jun 2024The aim of this study is to examine the perception, willingness to engage, and demand of community residents regarding the 'internet + nursing service' in a...
OBJECTIVE
The aim of this study is to examine the perception, willingness to engage, and demand of community residents regarding the 'internet + nursing service' in a designated pilot area, aiming to offer insights for the widespread adoption of the 'internet + nursing service' throughout China.
METHODS
A survey pertaining to the 'internet + nursing service' was conducted from March to April 2022. The study specifically targeted residents within two sub-districts of a city in the Jiangsu province. The sampling technique employed in this study was stratified random sampling.
RESULTS
Out of a total of 400 community residents selected from two sub-districts in this region, 378 provided valid responses, resulting in an effective rate of 94.5%. Within the study cohort, 80 participants (21.16%) demonstrated familiarity with the concept of 'internet + nursing service.' Additionally, 231 participants (61.11%) conveyed their willingness to adopt such services. Regarding service preferences, the primary demands were for health guidance, vital sign monitoring, and basic care. Challenges in implementing the service were attributed to concerns related to medical risks, personal safety for both nurses and patients, and potential breaches of privacy.
CONCLUSIONS
Residents in the pilot area exhibited a moderate awareness of the 'internet + nursing service,' with a relatively high willingness to embrace the program. There is a need for further refinement of pertinent laws, widespread dissemination of policies, and enhancements in the quality of nursing services. These measures aim to ensure that a greater number of community residents can avail themselves of improved home-based nursing services.
PubMed: 38912825
DOI: 10.1080/00325481.2024.2370233 -
Frontiers in Medicine 2024The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models....
The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.
PubMed: 38912338
DOI: 10.3389/fmed.2024.1409314 -
PeerJ. Computer Science 2024The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination....
The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off-chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper's decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identified in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper's implementation configurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes significantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the field.
PubMed: 38855255
DOI: 10.7717/peerj-cs.2060 -
Australasian Psychiatry : Bulletin of... Jun 2024
PubMed: 38842121
DOI: 10.1177/10398562241259631 -
The British Journal of Ophthalmology Jun 2024As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre... (Review)
Review
As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI from model-level and data-level perspectives. Moreover, we elucidate the potential consequences and case studies within the domain of ophthalmology. Model-level risks include knowledge leakage from the model and model safety under AI-specific attacks, while data-level risks involve unauthorised data collection and data accuracy concerns. Within the healthcare context, these risks can bear severe consequences, encompassing potential breaches of sensitive information, violating privacy rights and threats to patient safety. This paper not only highlights these challenges but also elucidates governance-driven solutions that adhere to AI and healthcare regulations. We advocate for preparedness against potential threats, call for transparency enhancements and underscore the necessity of clinical validation before real-world implementation. The objective of security and privacy improvement in generative AI warrants emphasising the role of ophthalmologists and other healthcare providers, and the timely introduction of comprehensive regulations.
PubMed: 38834290
DOI: 10.1136/bjo-2024-325167 -
Heliyon May 2024This review explores the Metaverse, focusing on user perceptions and emphasizing the critical aspects of usability, social influence, and interoperability within this... (Review)
Review
This review explores the Metaverse, focusing on user perceptions and emphasizing the critical aspects of usability, social influence, and interoperability within this emerging digital ecosystem. By integrating various academic perspectives, this analysis highlights the Metaverse's significant impact across various sectors, emphasizing its potential to reshape digital interaction paradigms. The investigation reveals usability as a cornerstone for user engagement, demonstrating how social dynamics profoundly influence user behaviors and choices within virtual environments. Furthermore, the study outlines interoperability as a paramount challenge, advocating for establishing unified protocols and technologies to facilitate seamless experiences across disparate Metaverse platforms. It advocates for the adoption of inclusive, ergonomically oriented designs aimed at enhancing user participation. It addresses the ethical and societal challenges posed by the Metaverse, including concerns related to digital harassment, invasive marketing practices, and breaches of privacy. Additionally, the review identifies existing gaps in the literature, particularly regarding the Metaverse's implications for healthcare, its impact on educational outcomes, and the urgent need for empirical data concerning its long-term effects on user psychology and behavior. By providing a comprehensive synthesis of the current understanding of user experiences and challenges within the Metaverse, this paper contributes to the academic dialogue, laying the groundwork for future research initiatives. It aims to steer the development of the Metaverse towards a trajectory that is ethically sound, socially responsible, inclusive, and aligned with societal expectations, thereby fostering a digital realm that upholds the highest standards of integrity and inclusivity.
PubMed: 38826724
DOI: 10.1016/j.heliyon.2024.e31413 -
Journal of Medical Internet Research May 2024Health care organizations worldwide are faced with an increasing number of cyberattacks and threats to their critical infrastructure. These cyberattacks cause... (Review)
Review
BACKGROUND
Health care organizations worldwide are faced with an increasing number of cyberattacks and threats to their critical infrastructure. These cyberattacks cause significant data breaches in digital health information systems, which threaten patient safety and privacy.
OBJECTIVE
From a sociotechnical perspective, this paper explores why digital health care systems are vulnerable to cyberattacks and provides sociotechnical solutions through a systematic literature review (SLR).
METHODS
An SLR using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was conducted by searching 6 databases (PubMed, Web of Science, ScienceDirect, Scopus, Institute of Electrical and Electronics Engineers, and Springer) and a journal (Management Information Systems Quarterly) for articles published between 2012 and 2022 and indexed using the following keywords: "(cybersecurity OR cybercrime OR ransomware) AND (healthcare) OR (cybersecurity in healthcare)." Reports, review articles, and industry white papers that focused on cybersecurity and health care challenges and solutions were included. Only articles published in English were selected for the review.
RESULTS
In total, 5 themes were identified: human error, lack of investment, complex network-connected end-point devices, old legacy systems, and technology advancement (digitalization). We also found that knowledge applications for solving vulnerabilities in health care systems between 2012 to 2022 were inconsistent.
CONCLUSIONS
This SLR provides a clear understanding of why health care systems are vulnerable to cyberattacks and proposes interventions from a new sociotechnical perspective. These solutions can serve as a guide for health care organizations in their efforts to prevent breaches and address vulnerabilities. To bridge the gap, we recommend that health care organizations, in partnership with educational institutions, develop and implement a cybersecurity curriculum for health care and intelligence information sharing through collaborations; training; awareness campaigns; and knowledge application areas such as secure design processes, phase-out of legacy systems, and improved investment. Additional studies are needed to create a sociotechnical framework that will support cybersecurity in health care systems and connect technology, people, and processes in an integrated manner.
Topics: Computer Security; Humans; Delivery of Health Care; Patient Safety
PubMed: 38820579
DOI: 10.2196/46904 -
Journal of Medical Internet Research May 2024Mobile health (mHealth) apps have the potential to enhance health care service delivery. However, concerns regarding patients' confidentiality, privacy, and security... (Review)
Review
BACKGROUND
Mobile health (mHealth) apps have the potential to enhance health care service delivery. However, concerns regarding patients' confidentiality, privacy, and security consistently affect the adoption of mHealth apps. Despite this, no review has comprehensively summarized the findings of studies on this subject matter.
OBJECTIVE
This systematic review aims to investigate patients' perspectives and awareness of the confidentiality, privacy, and security of the data collected through mHealth apps.
METHODS
Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a comprehensive literature search was conducted in 3 electronic databases: PubMed, Ovid, and ScienceDirect. All the retrieved articles were screened according to specific inclusion criteria to select relevant articles published between 2014 and 2022.
RESULTS
A total of 33 articles exploring mHealth patients' perspectives and awareness of data privacy, security, and confidentiality issues and the associated factors were included in this systematic review. Thematic analyses of the retrieved data led to the synthesis of 4 themes: concerns about data privacy, confidentiality, and security; awareness; facilitators and enablers; and associated factors. Patients showed discordant and concordant perspectives regarding data privacy, security, and confidentiality, as well as suggesting approaches to improve the use of mHealth apps (facilitators), such as protection of personal data, ensuring that health status or medical conditions are not mentioned, brief training or education on data security, and assuring data confidentiality and privacy. Similarly, awareness of the subject matter differed across the studies, suggesting the need to improve patients' awareness of data security and privacy. Older patients, those with a history of experiencing data breaches, and those belonging to the higher-income class were more likely to raise concerns about the data security and privacy of mHealth apps. These concerns were not frequent among patients with higher satisfaction levels and those who perceived the data type to be less sensitive.
CONCLUSIONS
Patients expressed diverse views on mHealth apps' privacy, security, and confidentiality, with some of the issues raised affecting technology use. These findings may assist mHealth app developers and other stakeholders in improving patients' awareness and adjusting current privacy and security features in mHealth apps to enhance their adoption and use.
TRIAL REGISTRATION
PROSPERO CRD42023456658; https://tinyurl.com/ytnjtmca.
Topics: Humans; Confidentiality; Telemedicine; Mobile Applications; Computer Security; Privacy
PubMed: 38820572
DOI: 10.2196/50715 -
Sensors (Basel, Switzerland) May 2024Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient...
Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual's identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.
Topics: Humans; Electromyography; Electronic Health Records; Male; Adult; Female; Computer Security; Deep Learning; Signal Processing, Computer-Assisted; Young Adult
PubMed: 38793994
DOI: 10.3390/s24103140 -
Sensors (Basel, Switzerland) May 2024Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet...
Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme.
PubMed: 38793843
DOI: 10.3390/s24102989