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NPJ Digital Medicine Mar 2023While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk...
While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients' trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients' privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an "avatar data", for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statistical information. Compared to Synthpop and CT-GAN, the Avatar method shows a similar level of signal maintenance while allowing to compute additional privacy metrics. In the light of distance-based privacy metrics, each individual produces an avatar simulation that is on average indistinguishable from 12 other generated avatar simulations for the clinical trial and 24 for the observational study. Data transformation using the Avatar method both preserves, the evaluation of the treatment's effectiveness with similar hazard ratios for the clinical trial (original HR = 0.49 [95% CI, 0.39-0.63] vs. avatar HR = 0.40 [95% CI, 0.31-0.52]) and the classification properties for the observational study (original AUC = 99.46 (s.e. 0.25) vs. avatar AUC = 99.84 (s.e. 0.12)). Once validated by privacy metrics, anonymous synthetic data enable the creation of value from sensitive pseudonymized data analyses by tackling the risk of a privacy breach.
PubMed: 36899082
DOI: 10.1038/s41746-023-00771-5 -
Health Information Management : Journal... Feb 2023The implementation of emerging technologies has resulted in an increase of data breaches in healthcare organisations, especially during the COVID-19 pandemic. Health...
BACKGROUND
The implementation of emerging technologies has resulted in an increase of data breaches in healthcare organisations, especially during the COVID-19 pandemic. Health information and cybersecurity managers need to understand if, and to what extent, breach types and locations are associated with their organisation's business type.
OBJECTIVE
To investigate if breach type and breach location are associated with business type, and if so, investigate how these factors affect information systems and protected health information in for-profit versus non-profit organisations.
METHOD
The quantitative study was performed using chi-square tests for association and post-hoc comparison of column proportions analysis on an archival data set of reported healthcare data breaches from 2020 to 2022. Data from the Department of Health and Human Services website was retrieved and each organisation classified as for-profit or non-profit.
RESULTS
For-profit organisations experienced a significantly higher number of breaches due to theft, and non-profit organisations experienced a significantly higher number of breaches due to unauthorised access. Furthermore, the number of breaches that occurred on laptops and paper/films was significantly higher in for-profit organisations.
CONCLUSION
While the threat level of hacking techniques is the same in for-profit and non-profit organisations, certain breach types are more likely to occur within specific breach locations based on the organisation's business type. To protect the privacy and security of medical information, health information and cybersecurity managers need to align with industry-leading frameworks and controls to prevent specific breach types that occur in specific locations within their environments.
PubMed: 36840419
DOI: 10.1177/18333583231158886 -
Healthcare (Basel, Switzerland) Feb 2023Physicians must respect their patients' rights to informed consent, privacy, access to medical records, non-discrimination, treatment by a qualified doctor, and a second...
BACKGROUND
Physicians must respect their patients' rights to informed consent, privacy, access to medical records, non-discrimination, treatment by a qualified doctor, and a second medical opinion. Compliance with patients' rights is mandatory, and legal breaches are considered medical malpractice under Romanian law. This is the first study to assess physicians' practices nationally and create a geographical map of legal compliance.
RESULTS
We examined survey responses of 2978 physicians, including 1587 general practitioners and 1391 attending physicians from high-risk specialties. According to the findings, 46.67% of physicians' practices adhered to the law. Physicians' practices were homogenous across the country's regions. General practitioners were significantly more legally compliant than attending physicians were. Additionally, 94.02% of the physicians acknowledged malpractice anxiety, whereas only 17.67% had been accused of malpractice.
CONCLUSIONS
Our findings emphasize the need for further research and to voice issues about Romanian physicians' low level of legal compliance. This study provides a starting point for future studies to evaluate the benefits of interventional strategies in this field. Healthcare facilities should provide physicians with easily available resources when they are unsure about their legal obligations, and establish an observer organization that can detect unlawful conduct. Interventions should concentrate on education programs and expert guidance.
PubMed: 36833032
DOI: 10.3390/healthcare11040499 -
Drug and Alcohol Dependence Mar 2023The prevalence of drug use in Muslim communities is difficult to estimate due to religious, social, and cultural prohibition toward drug use. With Islam affecting all... (Review)
Review
Barriers and facilitators to accessing inpatient and community substance use treatment and harm reduction services for people who use drugs in the Muslim communities: A systematic narrative review of studies on the experiences of people who receive services and service providers.
BACKGROUND
The prevalence of drug use in Muslim communities is difficult to estimate due to religious, social, and cultural prohibition toward drug use. With Islam affecting all aspects of life in the Muslim world, people who use drugs do it clandestinely to avoid stigma and exclusion from the community, leading to a low number of them seeking treatment for their drug use. This review explored the barriers and facilitators to accessing inpatient and community substance use treatment and harm reduction services for people who use drugs in Muslim communities.
METHODS
This review was in accordance with PRISMA. Seven databases were systematically searched for qualitative, quantitative, and mixed methods studies conducted in countries where at least 70% of the population were Muslim or where data were presented separately for Muslim communities in other countries. Eligible articles were reviewed, and key qualitative themes were abstracted and compared across studies and settings.
RESULTS
Twenty-four studies were included from Iran, Bangladesh, Afghanistan, Tajikistan, Kazakhstan, Kyrgyzstan, Egypt, Lebanon, and UAE. Two themes were identified: a psychosocial theme included denial of the problem severity, lack of trust in the treatment system, fear of breach in confidentiality and privacy, the need for community support, religion and women who use drugs. Additionally, an organizational theme included affordability, treatment Service characteristics, lack of Awareness, service providers' Attitudes, drug use registration and fear of legal consequences of drug use. Stigma was also identified as an over-arching theme. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of the included studies with where 12 of the studies met all 5 the quality criteria. No studies were excluded for having lower quality scores.
CONCLUSION
This review reflected how diverse the Muslim world is in drug use. It is important to use mosques to raise awareness on people who use drugs and reduce stigma. Providing holistic services for people who use drugs specially women will improve their access to treatment and harm reduction services in the Muslim world.
Topics: Humans; Female; Islam; Harm Reduction; Inpatients; Substance-Related Disorders; Social Stigma; Qualitative Research
PubMed: 36805826
DOI: 10.1016/j.drugalcdep.2023.109790 -
PloS One 2023More knowledge about the long-term impact of sperm donation is essential as the donor's attitude towards donation may change over time. Personal and social developments...
BACKGROUND
More knowledge about the long-term impact of sperm donation is essential as the donor's attitude towards donation may change over time. Personal and social developments may prompt a rethinking of previous actions and decisions, or even regret. Thus, the aim of this study was to explore the experiences and attitudes of men who were sperm donors more than 10 years ago.
METHODS
From May to September 2021, semi-structured, qualitative interviews were conducted with 23 former donors (> 10 years since last donation) from Cryos International sperm bank. Two participants were non-anonymous donors and 21 were anonymous. The interviews were conducted by phone or via video (mean 24 minutes). All interviews were recorded, transcribed verbatim and rendered anonymous. Data were analyzed using thematic analysis.
RESULTS
The analysis showed that most men had been donors for monetary and altruistic purposes, and now considered sperm donation as a closed chapter that was 'unproblematic and in the past'. Most men valued anonymity and emphasized the non-relatedness between donor and donor conceived offspring. Knowledge about recipients and donor offspring was seen as 'damaging' as it could create unwanted feelings of relatedness and responsibility towards them. All men acknowledged donor conceived persons' potential interests in knowing about their genetic heritage in order to understand appearance and personal traits, but also emphasized the donors' rights to anonymity. Potential breach of anonymity was generally considered 'highly problematic' as it was expected to disturb their families and force a relationship on them.
CONCLUSION
This study reports on former donors who might not have volunteered for research due to lack of interest or protection of privacy. The majority of men valued anonymity and clearly demarcated a line between sperm donation and fatherhood, which was enforced by not knowing about the donor offspring or recipients.
Topics: Humans; Male; Semen; Attitude; Tissue Donors; Spermatozoa; Denmark
PubMed: 36791066
DOI: 10.1371/journal.pone.0281022 -
Biomedical Materials & Devices (New... Feb 2023Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and... (Review)
Review
Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.
PubMed: 36785697
DOI: 10.1007/s44174-023-00063-2 -
Sensors (Basel, Switzerland) Jan 2023Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging... (Review)
Review
Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.
PubMed: 36772190
DOI: 10.3390/s23031151 -
Journal of Cloud Computing (Heidelberg,... 2023Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients' data managed by a workflow deployed...
Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients' data managed by a workflow deployed on the cloud need to be protected, and communication of such data across multiple stakeholders should also be secured. In general, security threats in cloud environments have been studied extensively. Such threats include data breaches, data loss, denial of service, service rejection, and malicious insiders generated from issues such as multi-tenancy, loss of control over data and trust. Supporting the security of a cloud workflow deployed and executed over a dynamic environment, across different platforms, involving different stakeholders, and dynamic data is a difficult task and is the sole responsibility of cloud providers. Therefore, in this paper, we propose an architecture and a formal model for security enforcement in cloud workflow orchestration. The proposed architecture emphasizes monitoring cloud resources, workflow tasks, and the data to detect and predict anomalies in cloud workflow orchestration using a multi-modal approach that combines deep learning, one class classification, and clustering. It also features an adaptation scheme to cope with anomalies and mitigate their effect on the workflow cloud performance. Our prediction model captures unsupervised static and dynamic features as well as reduces the data dimensionality, which leads to better characterization of various cloud workflow tasks, and thus provides better prediction of potential attacks. We conduct a set of experiments to evaluate the proposed anomaly detection, prediction, and adaptation schemes using a real COVID-19 dataset of patient health records. The results of the training and prediction experiments show high anomaly prediction accuracy in terms of precision, recall, and F1 scores. Other experimental results maintained a high execution performance of the cloud workflow after applying adaptation strategy to respond to some detected anomalies. The experiments demonstrate how the proposed architecture prevents unnecessary wastage of resources due to anomaly detection and prediction.
PubMed: 36691661
DOI: 10.1186/s13677-022-00387-2 -
Sensors (Basel, Switzerland) Jan 2023Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources...
Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption () technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.
Topics: Humans; Privacy; Bayes Theorem; Computer Security; Confidentiality; Machine Learning
PubMed: 36617116
DOI: 10.3390/s23010519 -
Sensors (Basel, Switzerland) Dec 2022Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute...
Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.
Topics: Humans; Blockchain; Electronic Health Records; Confidentiality; COVID-19; Computer Security; Health Records, Personal
PubMed: 36616613
DOI: 10.3390/s23010014