-
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 -
Women's Health (London, England) 2024Survivors of sexual assault and intimate partner violence often face many challenges in seeking/receiving healthcare and are often lost to follow up.
BACKGROUND
Survivors of sexual assault and intimate partner violence often face many challenges in seeking/receiving healthcare and are often lost to follow up.
OBJECTIVES
Our study objectives are to evaluate the feasibility, acceptability, and satisfaction of using telemedicine technology among sexual assault and intimate partner violence patients who present to a Canadian Emergency Department.
DESIGN
Qualitative research was conducted using a thematic approach.
METHODS
Patients were identified from a case registry of all sexual assault and intimate partner violence cases seen between 1 April 2020 and 31 March 2022 from an emergency department of a large Canadian hospital. Qualitative trauma-informed interviews were conducted with consenting participants. Thematic qualitative analyses were performed to investigate barriers and drivers of telemedicine for follow-up care.
RESULTS
Of the 1007 sexual assault and intimate partner violence patients seen during the study timeframe, 180 (8%) consented to be contacted for future research, and 10 completed an interview regarding telemedicine for follow-up care. All participants were cisgendered women, 5 (50%) experienced sexual assault, 6 (60%) physical assault, and 3 (30%) verbal assault. All knew their assailant, and 6 (60%) were assaulted by a current or former intimate partner. Three themes emerged as drivers of telemedicine use: increased comfort, increased convenience, and less time required for the appointment. Three thematic barriers to telemedicine use included lack of privacy from others, lack of safety from their assailant, and pressure to balance competing tasks during the appointment.
CONCLUSION
This study illustrated that telemedicine for sexual assault and intimate partner violence follow-up care is feasible, acceptable, and can improve patient satisfaction with follow-up care. Ensuring safety and privacy are key considerations when offering telemedicine as an appropriate option for survivors.
Topics: Humans; Telemedicine; Female; Qualitative Research; Intimate Partner Violence; Adult; Survivors; Canada; Sex Offenses; Middle Aged; Emergency Service, Hospital; Patient Satisfaction
PubMed: 38783826
DOI: 10.1177/17455057241252958 -
Medical Humanities May 2024In recent years, dating apps have become important allies in public health. In this paper, we explore the implications of partnering with dating apps for health...
In recent years, dating apps have become important allies in public health. In this paper, we explore the implications of partnering with dating apps for health promotion. We consider the opportunities and challenges inherent in these collaborations, paying special attention to privacy, trust, and user care in a digital environment.Despite their potential as targeted health promotion tools, dating apps raise significant ethical concerns, including the commodification of user data and privacy breaches, which highlight the complexities of blending healthcare initiatives with for-profit digital platforms. Furthermore, the paper delves into issues of discrimination, harassment and unequal access within these apps, factors which can undermine public health efforts.We develop a nuanced framework, emphasising the development of transparent data policies, the decoupling of content moderation from health initiatives and a commitment to combat discrimination. We underscore the importance of embedding app-based health initiatives within broader care pathways, ensuring comprehensive support beyond the digital domain. This essay offers vital insights for public health practitioners, app developers and policymakers navigating the intersection of digital innovation and healthcare.
PubMed: 38754966
DOI: 10.1136/medhum-2024-012901 -
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 -
Journal of Medical Internet Research May 2024A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on...
BACKGROUND
A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications.
OBJECTIVE
The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care.
METHODS
We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items.
RESULTS
The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data.
CONCLUSIONS
Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.
Topics: Delphi Technique; Humans; Natural Language Processing; Machine Learning; Delivery of Health Care; Medical Informatics
PubMed: 38739445
DOI: 10.2196/52399 -
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 -
Sensors (Basel, Switzerland) Apr 2024The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the...
The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.
PubMed: 38676050
DOI: 10.3390/s24082427 -
Cureus Mar 2024Electronic health records (EHR) have revolutionized healthcare by providing efficient access to patient information, but their implementation poses various challenges.... (Review)
Review
Electronic health records (EHR) have revolutionized healthcare by providing efficient access to patient information, but their implementation poses various challenges. This paper examines the ethical and legal issues surrounding EHR adoption, particularly focusing on the healthcare landscape in India. Ethical considerations, including patient autonomy, confidentiality, beneficence, and justice, must guide EHR implementation to protect patient rights and privacy. Legal issues such as medical errors, malpractice, data breaches, and billing inaccuracies underscore the importance of robust policies and security measures. Threats to EHRs, such as phishing attacks, malware, encryption vulnerabilities, and insider threats, emphasize the need for comprehensive cybersecurity strategies. Overcoming challenges in EHR implementation requires meticulous planning, financial investment, staff training, and stakeholder support. Despite the complexities involved, the benefits of EHR adoption in improving patient care and operational efficiency justify the efforts required to address legal, ethical, and technical concerns. Embracing EHRs while mitigating associated risks is essential for delivering high-quality healthcare in the digital age.
PubMed: 38646271
DOI: 10.7759/cureus.56518 -
JMIRx Med Apr 2024To address the pandemic, the Defense Health Agency (DHA) expanded its TRICARE civilian provider network by 30.1%. In 2022, the DHA Annual Report stated that TRICARE's...
BACKGROUND
To address the pandemic, the Defense Health Agency (DHA) expanded its TRICARE civilian provider network by 30.1%. In 2022, the DHA Annual Report stated that TRICARE's provider directories were only 80% accurate. Unlike Medicare, the DHA does not publicly reveal National Provider Identification (NPI) numbers. As a result, TRICARE's 9.6 million beneficiaries lack the means to verify their doctor's credentials. Since 2013, the Department of Health and Human Services' (HHS) Office of Inspector General (OIG) has excluded 17,706 physicians and other providers from federal health programs due to billing fraud, neglect, drug-related convictions, and other offenses. These providers and their NPIs are included on the OIG's List of Excluded Individuals and Entities (LEIE). Patients who receive care from excluded providers face higher risks of hospitalization and mortality.
OBJECTIVE
We sought to assess the extent to which TRICARE screens health care provider names on their referral website against criminal databases.
METHODS
Between January 1-31, 2023, we used TRICARE West's provider directory to search for all providers within a 5-mile radius of 798 zip codes (38 per state, ≥10,000 residents each, randomly entered). We then copied and pasted all directory results' first and last names, business names, addresses, phone numbers, fax numbers, degree types, practice specialties, and active or closed statuses into a CSV file. We cross-referenced the search results against US and state databases for medical and criminal misconduct, including the OIG-LEIE and General Services Administration's (GSA) SAM.gov exclusion lists, the HHS Office of Civil Rights Health Insurance Portability and Accountability Act (HIPAA) breach reports, 15 available state Medicaid exclusion lists (state), the International Trade Administration's Consolidated Screening List (CSL), 3 Food and Drug Administration (FDA) debarment lists, the Federal Bureau of Investigation's (FBI) list of January 6 federal defendants, and the OIG-HHS list of fugitives (FUG).
RESULTS
Our provider search yielded 111,619 raw results; 54 zip codes contained no data. After removing 72,156 (64.65%) duplicate entries, closed offices, and non-TRICARE West locations, we identified 39,463 active provider names. Within this baseline sample group, there were 2398 (6.08%) total matches against all exclusion and sanction databases, including 2197 on the OIG-LEIE, 2311 on the GSA-SAM.gov list, 2 on the HIPAA list, 54 on the state Medicaid exclusion lists, 69 on the CSL, 3 on the FDA lists, 53 on the FBI list, and 10 on the FUG.
CONCLUSIONS
TRICARE's civilian provider roster merits further scrutiny by law enforcement. Following the National Institute of Standards and Technology 800, the DHA can mitigate privacy, safety, and security clearance threats by implementing an insider threat management model, robust enforcement of the False Claims Act, and mandatory security risk assessments. These are the views of the author, not the Department of Defense or the US government.
PubMed: 38602314
DOI: 10.2196/52198 -
Sensors (Basel, Switzerland) Feb 2024The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to...
The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL's effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.
PubMed: 38474899
DOI: 10.3390/s24051364