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Abstract

The integration of cutting-edge technologies into health literacy and medical practice presents unprecedented opportunities and ethical challenges. This chapter delves into the technoethical considerations crucial for navigating this new landscape, guided by a posthumanist framework. Embracing this framework encourages us to reevaluate traditional boundaries, inviting a more inclusive understanding of humanity's relationship with technology. The chapter also explores the intricate relationship between technology and ethics, advocating for a technoethical framework that ensures technological advancements in healthcare are employed ethically, responsibly, and with a deep understanding of their societal impact. By doing so, this chapter serves as a guiding beacon for healthcare professionals, technologists, ethicists, and policymakers, urging a future where technology and human values coalesce to foster a healthcare ecosystem that is not only advanced but also compassionate, equitable, and ethically grounded.

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Most cited references 66

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Health information seeking in the digital age: An analysis of health information seeking behavior among US adults

Ann O Amuta, Cláudia Álvares, Wura Jacobs … (2017)

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The promise of machine learning in predicting treatment outcomes in psychiatry

Adam Chekroud, Julia Bondar, Jaime Delgadillo … (2021)

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

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The Relationship between Health Literacy and Health Disparities: A Systematic Review

Sarah Mantwill, Silvia Monestel-Umaña, Peter J. Schulz (2015)

Objectives Health literacy is commonly associated with many of the antecedents of health disparities. Yet the precise nature of the relationship between health literacy and disparities remains unclear. A systematic review was conducted to better understand in how far the relationship between health literacy and health disparities has been systematically studied and which potential relationships and pathways have been identified. Methods Five databases, including PubMed/MEDLINE and CINAHL, were searched for peer-reviewed studies. Publications were included in the review when they (1) included a valid measure of health literacy, (2) explicitly conceived a health disparity as related to a social disparity, such as race/ethnicity or education and (3) when results were presented by comparing two or more groups afflicted by a social disparity investigating the effect of health literacy on health outcomes. Two reviewers evaluated each study for inclusion and abstracted relevant information. Findings were ordered according to the disparities identified and the role of health literacy in explaining them. Results 36 studies were included in the final synthesis. Most of the studies investigated racial/ethnic disparities, followed by some few studies that systematically investigated educational disparities. Some evidence was found on the mediating function of health literacy on self-rated health status across racial/ethnic and educational disparities, as well as on the potential effect of health literacy and numeracy on reducing racial/ethnic disparities in medication adherence and understanding of medication intake. Conclusion Overall the evidence on the relationship between health literacy and disparities is still mixed and fairly limited. Studies largely varied with regard to health(-related) outcomes under investigation and the health literacy assessments used. Further, many studies lacked a specific description of the nature of the disparity that was explored and a clear account of possible pathways tested.