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Journal of Medical Internet Research Jun 2024Photographs from medical case reports published in academic journals have previously been found in online image search results. This means that patient photographs... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Photographs from medical case reports published in academic journals have previously been found in online image search results. This means that patient photographs circulate beyond the original journal website and can be freely accessed online. While this raises ethical and legal concerns, no systematic study has documented how often this occurs.
OBJECTIVE
The aim of this cross-sectional study was to provide systematic evidence that patient photographs from case reports published in medical journals appear in Google Images search results. Research questions included the following: (1) what percentage of patient medical photographs published in case reports were found in Google Images search results? (2) what was the relationship between open access publication status and image availability? and (3) did the odds of finding patient photographs on third-party websites differ between searches conducted in 2020 and 2022?
METHODS
The main outcome measure assessed whether at least 1 photograph from each case report was found on Google Images when using a structured search. Secondary outcome variables included the image source and the availability of images on third-party websites over time. The characteristics of medical images were described using summary statistics. The association between the source of full-text availability and image availability on Google Images was tested using logistic regressions. Finally, we examined the trend of finding patient photographs using generalized estimating equations.
RESULTS
From a random sample of 585 case reports indexed in PubMed, 186 contained patient photographs, for a total of 598 distinct images. For 142 (76.3%) out of 186 case reports, at least 1 photograph was found in Google Images search results. A total of 18.3% (110/598) of photographs included eye, face, or full body, including 10.9% (65/598) that could potentially identify the patient. The odds of finding an image from the case report online were higher if the full-text paper was available on ResearchGate (odds ratio [OR] 9.16, 95% CI 2.71-31.02), PubMed Central (OR 7.90, 95% CI 2.33-26.77), or Google Scholar (OR 6.07, 95% CI 2.77-13.29) than if the full-text was available solely through an open access journal (OR 5.33, 95% CI 2.31-12.28). However, all factors contributed to an increased risk of locating patient images online. Compared with the search in 2020, patient photographs were less likely to be found on third-party websites based on the 2022 search results (OR 0.61, 95% Cl 0.43-0.87).
CONCLUSIONS
A high proportion of medical photographs from case reports was found on Google Images, raising ethical concerns with policy and practice implications. Journal publishers and corporations such as Google are best positioned to develop an effective remedy. Until then, it is crucial that patients are adequately informed about the potential risks and benefits of providing consent for clinicians to publish their images in medical journals.
Topics: Cross-Sectional Studies; Humans; Photography; Internet
PubMed: 38913416
DOI: 10.2196/55352 -
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 -
Frontiers in Public Health 2024Digital health disparities continue to affect marginalized populations, especially older adults, individuals with low-income, and racial/ethnic minorities, intensifying...
Digital health disparities continue to affect marginalized populations, especially older adults, individuals with low-income, and racial/ethnic minorities, intensifying the challenges these populations face in accessing healthcare. Bridging this digital divide is essential, as digital access and literacy are social determinants of health that can impact digital health use and access to care. This article discusses the potential of leveraging community Wi-Fi and spaces to improve digital access and digital health use, as well as the challenges and opportunities associated with this strategy. The existing limited evidence has shown the possibility of using community Wi-Fi and spaces, such as public libraries, to facilitate telehealth services. However, privacy and security issues from using public Wi-Fi and spaces remain a concern for librarians and healthcare professionals. To advance digital equity, efforts from multilevel stakeholders to improve users' digital access and literacy and offer tailored technology support in the community are required. Ultimately, leveraging community Wi-Fi and spaces offers a promising avenue to expand digital health accessibility and use, highlighting the critical role of collaborative efforts in overcoming digital health disparities.
Topics: Humans; Telemedicine; Health Services Accessibility; Healthcare Disparities; Digital Divide; Digital Health
PubMed: 38912273
DOI: 10.3389/fpubh.2024.1418627 -
Frontiers in Public Health 2024Digital health literacy (DHL) is a key competency for individuals' daily decisions toward their health behavior and wellbeing. While there is much focus on health...
INTRODUCTION
Digital health literacy (DHL) is a key competency for individuals' daily decisions toward their health behavior and wellbeing. While there is much focus on health literacy (HL) among the general population, teachers have been rarely addressed. Given the shortages in the teaching workforce in Europe and the impact of demanding working conditions on their health, it is important to address DHL in teachers. This paper examines the DHL of primary and secondary teachers and its associations with sociodemographic and school-related factors.
METHODS
An online cross-sectional study was conducted with 1,600 German primary and secondary school teachers between October and December 2022. To assess DHL, the Digital Health Literacy Instrument (DHLI) including seven subscales was used. Statistical analyses were conducted on item and subscale level and an overall DHL score was calculated. Next to descriptive analyses, bivariate and regression analyses were conducted to explore potential associations with sociodemographic and school-related factors.
RESULTS
The frequency of difficulty in using digital health information varied across DHL dimensions and was greatest for (70.9%) and (40.0%). In multivariate analysis, females more often reported a sufficient ability of (OR = 1.61, CI = 1.05-2.48), while males more often reported a sufficient ability to (OR = 0.45, CI = 0.27-0.75). Teachers with leadership positions more often reported a sufficient ability in (OR = 1.78, CI = 1.07-2.98). Regarding the ability to of online health-related information, no associations with a predictor variable were found.
DISCUSSION
The results suggest that it is important to examine the individual dimensions of DHL and their distinct associations with sociodemographic and school-level factors, rather than just to rely on the overall level of DHL. The differential patterns identified in this study suggest a greater intervention need for teachers from higher age groups, primary and secondary general schools, and those without leadership roles. However, based on the limited predictive power of the variables included, further individual and school-level factors and their potential association with DHL should be investigated in the future. The promotion of DHL should be integrated into both teacher education and in-service training.
Topics: Humans; School Teachers; Male; Female; Health Literacy; Cross-Sectional Studies; Adult; Middle Aged; Germany; Surveys and Questionnaires; Schools
PubMed: 38912263
DOI: 10.3389/fpubh.2024.1334263 -
Risk Management and Healthcare Policy 2024Growing cyberattacks have made it more challenging to maintain healthcare information system (HIS) security in medical institutes, especially for hospitals that provide...
BACKGROUND
Growing cyberattacks have made it more challenging to maintain healthcare information system (HIS) security in medical institutes, especially for hospitals that provide patient portals to access patient information, such as electronic health record (EHR).
OBJECTIVE
This work aims to evaluate the patient portal security risk of Taiwan's EEC (EMR Exchange Center) member hospitals and analyze the association between patient portal security, hospital location, contract category and hospital type.
METHODS
We first collected the basic information of EEC member hospitals, including hospital location, contract category and hospital type. Then, the patient portal security of individual hospitals was evaluated by a well-known vulnerability scanner, UPGUARD, to assess website if vulnerable to high-level attacks such as denial of service attacks or ransomware attacks. Based on their UPSCAN scores, hospitals were classified into four security ratings: absolute low risk, low to medium risk, medium to high risk and high risk. Finally, the associations between security rating, contract category and hospital type were analyzed using chi-square tests.
RESULTS
We surveyed a total of 373 EEC member hospitals. Among them, 20 hospital patient portals were rated as "absolute low risk", 104 hospital patient portals as "low to medium risk", 99 hospital patient portals as "medium to high risk" and 150 hospital patient portals as "high risk". Further investigation revealed that the patient portal security of EEC member hospitals was significantly associated with the contract category and hospital type (<0.001).
CONCLUSION
The analysis results showed that large-scale hospitals generally had higher security levels, implying that the security of low-tier and small-scale hospitals may warrant reinforcement or strengthening. We suggest that hospitals should pay attention to the security risk assessment of their patient portals to preserve patient information privacy.
PubMed: 38910900
DOI: 10.2147/RMHP.S463408 -
Proceedings (Baylor University. Medical... 2024Colorectal cancer (CRC) presents significant mortality risks, underscoring the urgency of timely diagnosis and intervention. Advanced stages of CRC are managed through...
Colorectal cancer (CRC) presents significant mortality risks, underscoring the urgency of timely diagnosis and intervention. Advanced stages of CRC are managed through chemotherapy, targeted therapy, immunotherapy, radiotherapy, and surgery. Immunotherapy, while effective in bolstering the immune system against cancer cells, often carries toxic side effects, including colitis. This study aimed to evaluate the incidence of colitis in patients with metastatic CRC (mCRC) undergoing various immunotherapy treatments. Through a systematic search of Google Scholar and PubMed databases from inception until November 2023, nine relevant studies were identified. Subgroup analyses revealed a higher incidence of colitis, particularly in patients treated with anti-cytotoxic T-lymphocyte-associated molecule-4 (anti-CTLA-4) and combination therapies compared to monotherapy with programmed cell death receptor-1 (PD-1) or programmed cell death ligand receptor-1 (PDL-1) inhibitors. Notably, naive-treated metastatic CRC patients exhibited elevated colitis incidences compared to those previously treated. In conclusion, anti-CTLA-4 and combination therapies, such as nivolumab plus ipilimumab, were associated with increased colitis occurrences in metastatic CRC patients, highlighting the need for vigilant monitoring and management strategies, especially in immunotherapy-naive individuals.
PubMed: 38910824
DOI: 10.1080/08998280.2024.2342723 -
BMJ Open Jun 2024Sub-Saharan Africa (SSA) regions have the highest burden of cervical cancer (CC), accounting for nearly a quarter of global mortality. Many women in SSA are reluctant to...
INTRODUCTION
Sub-Saharan Africa (SSA) regions have the highest burden of cervical cancer (CC), accounting for nearly a quarter of global mortality. Many women in SSA are reluctant to access CC screening because they are uncomfortable exposing their private parts to healthcare providers. The perception of women who have experienced self-sampling in SSA is yet to be reviewed. This scoping review will explore the literature on the perception and attitude of women towards methods of collecting cervicovaginal samples for human papillomavirus (HPV) testing in SSA.
METHODS AND ANALYSIS
An extensive search using the Arksey and O'Malley framework will be conducted. The search criteria will be limited to original research conducted in community or clinical settings in SSA within the last 10 years. Four databases, namely, PUBMED, Cochrane, African Journals Online and Google Scholar, will be searched. Two independent persons (UIAB and DOO) will screen the titles and abstracts and later full texts using population, intervention, comparison and outcome criteria. IOMB will serve as a tiebreaker whenever there is no agreement on the choice of eligibility criteria. The screening process will be presented using Preferred Reporting Items for Systematic Reviews and Meta-Analyses for the scoping review flow format. The descriptive analysis of eligible studies for scoping reviews will be summarised. We will describe themes of attitude and perception covering pain, embarrassment, privacy and comfortability, willingness to self-sample, anxiety and confidence.
ETHICS AND DISSEMINATION
This is a scoping review protocol and does not require ethical approval. Findings from this review will be disseminated through peer-reviewed publications, the production of policy briefs, and presentations at local and international conferences.
Topics: Humans; Female; Africa South of the Sahara; Papillomavirus Infections; Uterine Cervical Neoplasms; Specimen Handling; Research Design; Early Detection of Cancer; Vaginal Smears; Papillomaviridae; Review Literature as Topic; Mass Screening; Human Papillomavirus Viruses
PubMed: 38910004
DOI: 10.1136/bmjopen-2024-085408 -
Journal of the American Medical... Jun 2024This article proposes a framework for examining the ethical and legal concerns for using artificial intelligence (AI) in post-acute and long-term care (PA-LTC). It...
This article proposes a framework for examining the ethical and legal concerns for using artificial intelligence (AI) in post-acute and long-term care (PA-LTC). It argues that established frameworks on health, AI, and the law should be adapted to specific care contexts. For residents in PA-LTC, their social, psychological, and mobility needs should act as a gauge for examining the benefits and risks of integrating AI into their care. Using those needs as a gauge, 4 areas of particular concern are identified. First, the threat that AI poses to the autonomy of residents can undermine their core needs. Second, how discrimination and bias in algorithmic decision-making can undermine Medicare coverage for PA-LTC, causing doctors' recommendations to be ignored and denying residents the care they are entitled to. Third, privacy rules concerning data use may undermine developers' ability to train accurate AI systems, limiting their usefulness in PA-LTC contexts. Fourth, the importance of obtaining consent before AI is used and discussions about how that care should continue if there are concerns about an ongoing decline in cognition. Together, these considerations elevate existing frameworks and adapt them to the context-specific case of PA-LTC. It is hoped that future research will examine the legal implications of these matters in each of these specific cases.
PubMed: 38909630
DOI: 10.1016/j.jamda.2024.105105 -
BMC Medical Research Methodology Jun 2024Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or...
BACKGROUND
Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention.
METHODS
Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method. For this study, data pipelines were built which extract data from OMOP, convert them into time series format, learn temporal rules by 2 statistical algorithms (Markov chain, TARM) and 3 algorithms of causal discovery (DYNOTEARS, J-PCMCI+, LiNGAM) and map the outputs into Synthea graphs. The graphs are evaluated quantitatively by their individual and relative complexity and qualitatively by medical experts.
RESULTS
The algorithms were found to learn qualitatively and quantitatively different graph representations. Whereas the Markov chain results in extremely large graphs, TARM, DYNOTEARS, and J-PCMCI+ were found to reduce the data dimension during learning. The MultiGroupDirect LiNGAM algorithm was found to not be applicable to the problem statement at hand.
CONCLUSION
Only TARM and DYNOTEARS are practical algorithms for real-world data in this use case. As causal discovery is a method to debias purely statistical relationships, the gradient-based causal discovery algorithm DYNOTEARS was found to be most suitable.
Topics: Humans; Algorithms; Electronic Health Records; Markov Chains; Medical Informatics
PubMed: 38909216
DOI: 10.1186/s12874-024-02257-8 -
Scientific Reports Jun 2024Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely...
Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e., data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.
Topics: Humans; Longitudinal Studies; Data Analysis; Artificial Intelligence
PubMed: 38909025
DOI: 10.1038/s41598-024-62102-2