BMC Medical Ethics最新文献

In case of an emergency, health insurance in Germany provides easy access to medical care in emergency departments. Over 100,000 people do not have health insurance for various reasons. They are repeatedly refused treatment in emergency rooms as their right to care outside of regular insurance is often unknown or ignored.

Background: The involvement of pregnant women in vaccine clinical trials presents unique challenges for the informed consent process. We explored the expectations and experiences of the pregnant women, spouses/partners, health workers and stakeholders of the consent process during a Group B Streptococcus maternal vaccine trial.

Methods: We interviewed 56 participants including pregnant women taking part in the trial, women not in the trial, health workers handling the trial procedures, spouses, and community stakeholders. We conducted 13 in-depth interviews and focus group discussions with 23 women in the trial, in-depth interviews with 5 spouses, and 5 women not in the trial, key informant interviews with 5 health workers and 5 other stakeholders were undertaken.

Results: Decision-making by a pregnant woman to join a trial was done in consultation with spouse, parents, siblings, or trusted health workers. Written study information was appreciated by all but they suggested the use of audio and visual presentation to enhance understanding. Women stressed the need to ensure that their male partners received study information before their pregnant partners joined a clinical trial. Confidentiality in research was emphasised differently by individual participants; while some emphasised it for self, others were keen to protect their family members from being exposed, for allowing them to be involved in research. However, others wanted their community participation to be acknowledged.

Conclusion: We found that pregnant women make decisions to join a clinical trial after consulting with close family. Our findings suggest the need for an information strategy which informs not only the pregnant woman, but also her family about the research she is invited to engage in.

Background: A morally sound framework for benefit-sharing is crucial to minimize research exploitation for research conducted in developing countries. However, in practice, it remains uncertain which stakeholders should be involved in the decision-making process regarding benefit-sharing and what the implications might be. Therefore the study aimed to empirically propose a framework for benefit-sharing negotiations in research by taking HIV vaccine trials as a case.

Methods: The study was conducted in Tanzania using a case study design and qualitative approaches. Data were collected using in-depth interviews (IDI) and focus group discussions (FGD). A total of 37 study participants were selected purposively comprising institutional review board (IRB) members, researchers, community advisory board (CAB) members, a policymaker, and HIV/AIDS advocates. Deductive and inductive thematic analysis approaches were deployed to analyze collected data with the aid of MAXQDA version 20.4.0 software.

Results: The findings indicate a triangular relationship between the research community, researched community and intermediaries. However, the relationship ought to take into consideration the timing of negotiations, the level of understanding between parties and the phase of the clinical trial. The proposed framework operationalize partnership interactions in community-based participatory research.

Conclusion: In the context of this study, the suggested framework incorporates the research community, the community being researched, and intermediary parties. The framework would guarantee well-informed and inclusive decision-making regarding benefit-sharing in HIV vaccine trials and other health-related research conducted in resource-limited settings.

Background: The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers' perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.

Methods: A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.

Results: Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.

Conclusions: This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.