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Heliyon Dec 2023Traditional cloud-centric approaches to medical data sharing pose risks related to real-time performance, security, and stability. Medical and healthcare data encounter...
Traditional cloud-centric approaches to medical data sharing pose risks related to real-time performance, security, and stability. Medical and healthcare data encounter challenges like data silos, privacy breaches, and transmission latency. In response to these challenges, this paper introduces a blockchain-based framework for trustworthy medical data sharing in edge computing environments. Leveraging healthcare consortium edge blockchains, this framework enables fine-grained access control to medical data. Specifically, it addresses the real-time, multi-attribute authorization challenge in CP-ABE through a Distributed Attribute Authorization strategy (DAA) based on blockchain. Furthermore, it tackles the key security issues in CP-ABE through a Distributed Key Generation protocol (DKG) based on blockchain. To address computational resource constraints in CP-ABE, we enhance a Distributed Modular Exponentiation Outsourcing algorithm (DME) and elevate its verifiable probability to "1". Theoretical analysis establishes the IND-CPA security of this framework in the Random Oracle Model. Experimental results demonstrate the effectiveness of our solution for resource-constrained end-user devices in edge computing environments.
PubMed: 38090001
DOI: 10.1016/j.heliyon.2023.e22542 -
ISA Transactions Feb 2024Advanced 5 G and 6 G technologies have accelerated the adoption of the Internet of Things (IoT) and are a priority in providing support for high-speed communication...
Advanced 5 G and 6 G technologies have accelerated the adoption of the Internet of Things (IoT) and are a priority in providing support for high-speed communication and fast data analysis. One of IoT networks benefits is automated networking, which unfortunately increases the risk of security, integrity, and privacy breaches. Therefore, in this paper, we propose a weighted stacked ensemble model combining deep convolutional generative adversarial and bidirectional long short-term memory networks. The proposed model has been regularized, and hyperparameter tuning has been performed. The tuned model is then evaluated on four publicly available current IoT datasets. The proposed model exhibits significant improvement in standard performance measures for both binary and multiclass classification. Generalization error has been reduced by a rate of 0.005% and to overcome the issue of overfitting, a L2 regularization technique has been deployed. The overall Accuracy of the model on various datasets is 99.99% for BOT-IoT, 99.08% for IoT23, 99.82% for UNSWNB15, and 99.96% for ToN_IoT, respectively, alongside improvements in Precision, Recall, and F1-score.
PubMed: 38105170
DOI: 10.1016/j.isatra.2023.12.005 -
Ciencia & Saude Coletiva May 2024The conceptions, values, and experiences of students from public and private high schools in two Brazilian state capitals, Vitória-ES and Campo Grande-MS, were analyzed...
The conceptions, values, and experiences of students from public and private high schools in two Brazilian state capitals, Vitória-ES and Campo Grande-MS, were analyzed regarding digital control and monitoring between intimate partners and the unauthorized exposure of intimate material on the Internet. Data from eight focus groups with 77 adolescents were submitted to thematic analysis, complemented by a questionnaire answered by a sample of 530 students. Most students affirmed that they do not tolerate the control/monitoring and unauthorized exposure of intimate materials but recognized that such activity is routine. They point out jealousy, insecurity, and "curiosity" as their main reasons. They detail the various dynamics of unauthorized exposure of intimate material and see it as a severe invasion of privacy and a breach of trust between partners. Their accounts suggest that such practices are gender violence. They also reveal that each platform has its cultural appropriation and that platforms used by the family, such as Facebook, cause more significant damage to the victim's reputation.
Topics: Humans; Brazil; Adolescent; Female; Male; Surveys and Questionnaires; Students; Sexual Partners; Focus Groups; Internet; Intimate Partner Violence; Privacy; Gender-Based Violence; Interpersonal Relations; Jealousy; Schools; Young Adult
PubMed: 38747777
DOI: 10.1590/1413-81232024295.15552022 -
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 -
Australasian Psychiatry : Bulletin of... Jun 2024
PubMed: 38842121
DOI: 10.1177/10398562241259631 -
Nursing Research and Practice 2024Bedside nursing handover is a recognized nursing practice that involves conducting shift change communication at the patient's bedside to enhance communication safety....
BACKGROUND
Bedside nursing handover is a recognized nursing practice that involves conducting shift change communication at the patient's bedside to enhance communication safety. Understanding the perceptions of both patients and nurses regarding bedside handover is crucial in identifying the key principles for developing and implementing effective bedside handover protocols. However, there is currently a lack of comprehensive evidence that summarizes and evaluates studies focused on qualitative approaches for gaining insights into the perceptions of both nurses and patients.
PURPOSE
This meta-synthesis review aims to identify, synthesize, and evaluate the quality of primary qualitative studies on the perceptions of patients and nurses about bedside nursing handover.
METHODS
A meta-synthesis review was conducted to identify qualitative studies that reported patients and nurses' perceptions about bedside handover using seven electronic databases, including CINAHL, PsycINFO, Embase, Education Database (ProQuest), Web of Science, The Cochrane Library, and PubMed, from January 2013 to November 2023. The authors independently selected reviews, extracted data, and evaluated the quality of included studies using the 10-item JBI Qualitative Assessment and Review Instrument tool.
RESULTS
A total of 871 articles were retrieved, of which 13 met the inclusion and exclusion criteria. These studies identified three main themes: (1) facilitators of bedside nursing handover, (2) barriers to bedside nursing handover, and (3) strategies to maintain confidentiality during bedside handover.
CONCLUSION
This study systematically reviewed and integrated the perceptions of patients and nurses about bedside handover. Based on nurses' perceptions, the combined findings highlight the facilitators of bedside handover, including developing partnership interaction between nurses and patients, promoting professionalism, and enhancing emotional communication among nurses. From the patients' viewpoint, the synthesized findings emphasize the facilitators of bedside handover, including acknowledging the expertise, professionalism, and humanity of the nursing profession, ensuring a sense of safety, satisfaction, and confidence in the care received, as well as promoting individualized nursing care. In the context of barriers to bedside handover, both nurses and patients perceive breaches of confidentiality and privacy violations as significant barriers. When it comes to maintaining confidentiality during bedside handovers, it is important to consider patients' preferences. Patients often prefer handovers to take place in a private setting. From the nurses' perspective, it is important to inquire with patients about their preference for the presence of caregivers, and to conduct private handovers for sensitive issues away from the bedside. . Clinicians should carefully evaluate the barriers and facilitators in this meta-synthesis prior to implementing bedside handover. . This study is registered in PROSPERO with Protocol registration ID: CRD42024514615.
PubMed: 38716049
DOI: 10.1155/2024/3208747 -
Journal of Healthcare Risk Management :... Oct 2023Creating adequate safeguards for physical and online locations (e.g., desktop computers, network servers) where protected health information (PHI) may be breached is...
Creating adequate safeguards for physical and online locations (e.g., desktop computers, network servers) where protected health information (PHI) may be breached is critical for management within entities compliant with the Health Information Portability and Accountability Act (HIPAA). With the increasing complexity of cyber breaches and budgetary issues, prioritizing which locations require the most immediate attention by top management through a data-driven model is more important than ever. Using CORAS threat modeling and five methods for multi-criteria decision-making, these locations were ranked from greatest to least risk of data breaches. Statistical methods were subsequently used for consistency and robustness checks. The findings illustrate that each type of covered entity under HIPAA must prioritize a different set of locations to safeguard first: health care providers must focus on the security of network servers, other portable electronic devices, and category of others (i.e., miscellaneous locations); health plans must focus on the security of paper and films, network servers, and others; and business associates must focus on the security of category of others, network servers, and other portable electronic devices. Combined with data on the source of the breaches (external vs. internal) and type of threats (e.g., hacking, theft), these findings provide recommendations for risk identification for privacy officers across health care.
Topics: United States; Humans; Confidentiality; Health Insurance Portability and Accountability Act; Social Responsibility; Health Facilities; Health Personnel; Computer Security
PubMed: 37616038
DOI: 10.1002/jhrm.21555 -
Health Expectations : An International... Feb 2024General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance,...
INTRODUCTION
General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research. In this study, we explored community views on sharing general practice data for secondary purposes, including research, to establish what concerns and conditions need to be addressed in the process of developing a social licence to support such use.
METHODS
We used a mixed-methods approach with focus groups (November-December 2021), followed by a cross-sectional survey (March-April 2022).
RESULTS
The participants in this study strongly supported sharing general practice data with the clinicians responsible for their care, and where there were direct benefits for individual patients. Over 90% of survey participants (N = 2604) were willing to share their general practice information to directly support their health care, that is, for the primary purpose of collection. There was less support for sharing data for secondary purposes such as research and health service planning (36% and 45% respectively in broad agreement) or for linking general practice data to data in the education, social services and criminal justice systems (30%-36%). A substantial minority of participants were unsure or could not see how benefits would arise from sharing data for secondary purposes. Participants were concerned about the potential for privacy breaches, discrimination and data misuse and they wanted greater transparency and an opportunity to consent to data release.
CONCLUSION
The findings of this study suggest that the public may be more concerned about sharing general practice data for secondary purposes than they are about sharing data collected in other settings. Sharing general practice data more broadly will require careful attention to patient and public concerns, including focusing on the factors that will sustain trust and legitimacy in general practice and GPs.
PATIENT AND PUBLIC CONTRIBUTION
Members of the public were participants in the study. Data produced from their participation generated study findings.
CLINICAL TRIAL REGISTRATION
Not applicable.
Topics: Humans; Cross-Sectional Studies; Information Dissemination; Focus Groups; Delivery of Health Care; General Practice
PubMed: 38361335
DOI: 10.1111/hex.13984 -
PloS One 2024Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of...
OBJECTIVES
Synthetic datasets are artificially manufactured based on real health systems data but do not contain real patient information. We sought to validate the use of synthetic data in stroke and cancer research by conducting a comparison study of cancer patients with ischemic stroke to non-cancer patients with ischemic stroke.
DESIGN
retrospective cohort study.
SETTING
We used synthetic data generated by MDClone and compared it to its original source data (i.e. real patient data from the Ottawa Hospital Data Warehouse).
OUTCOME MEASURES
We compared key differences in demographics, treatment characteristics, length of stay, and costs between cancer patients with ischemic stroke and non-cancer patients with ischemic stroke. We used a binary, multivariable logistic regression model to identify risk factors for recurrent stroke in the cancer population.
RESULTS
Using synthetic data, we found cancer patients with ischemic stroke had a lower prevalence of hypertension (52.0% in the cancer cohort vs 57.7% in the non-cancer cohort, p<0.0001), and a higher prevalence of chronic obstructive pulmonary disease (COPD: 8.5% vs 4.7%, p<0.0001), prior ischemic stroke (1.7% vs 0.1%, p<0.001), and prior venous thromboembolism (VTE: 8.2% vs 1.5%, p<0.0001). They also had a longer length of stay (8 days [IQR 3-16] vs 6 days [IQR 3-13], p = 0.011), and higher costs associated with their stroke encounters: $11,498 (IQR $4,440 -$20,668) in the cancer cohort vs $8,084 (IQR $3,947 -$16,706) in the non-cancer cohort (p = 0.0061). A multivariable logistic regression model identified 5 predictors for recurrent ischemic stroke in the cancer cohort using synthetic data; 3 of the same predictors identified using real patient data with similar effect measures. Summary statistics between synthetic and original datasets did not significantly differ, other than slight differences in the distributions of frequencies for numeric data.
CONCLUSION
We demonstrated the utility of synthetic data in stroke and cancer research and provided key differences between cancer and non-cancer patients with ischemic stroke. Synthetic data is a powerful tool that can allow researchers to easily explore hypothesis generation, enable data sharing without privacy breaches, and ensure broad access to big data in a rapid, safe, and reliable fashion.
Topics: Humans; Retrospective Studies; Big Data; Stroke; Neoplasms; Risk Factors; Ischemic Stroke; Pulmonary Disease, Chronic Obstructive
PubMed: 38324588
DOI: 10.1371/journal.pone.0295921 -
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