Paradigms in Operation: Pharmaceutical Benefit Assessments in England and Germany

The assessment of the benefits of pharmaceutical products through health technology assessments (HTAs) has become a feature of health care decision-making in numerous OECD countries, including England and Germany. Assessment outcomes vary between countries but, to date, there is a lack of research on the factors that affect those assessments. This thesis addresses this shortcoming by examining what determines the outcome of pharmaceutical benefit assessments in two countries that employ formalised HTA procedures. It takes a novel theoretical approach by employing a framework of policy paradigms to explain an empirical phenomenon other than policy change. The study presents a qualitative analysis that compares the reasoning processes that led to assessment outcomes in ten of the same cases of pharmaceuticals in England and Germany. It finds that benefit assessment outcomes are determined by how a similar set of themes around evidence gets interpreted and framed by a HTA body, e.g. the National Institute for Health and Care Excellence (NICE) in England and the Federal Joint Committee (FJC) in Germany. The study explains the differences in addressing a similar set of themes around evidence by reference to different HTA paradigms that are applied, namely a cost effectiveness paradigm in England and a patient relevance paradigm in Germany. The thesis makes a significant theoretical contribution because it demonstrates that policy paradigms can be used to explain empirical phenomena other than policy change. This requires an analysis of how paradigms are articulated in ‘normal decision-making’, much akin to Kuhn’s analysis on the connection between ‘normal science’ and paradigms. The study calls for an expansion of the current use of policy paradigms to include how they are operationalised in practice as this leads to a better understanding of the crucial elements of a paradigm

The Pathogenic TSH β-Subunit Variant C105Vfs114X Causes a Modified Signaling Profile at TSHR

1) Background: Central congenital hypothyroidism (CCH) is a rare endocrine disorder that can be caused by mutations in the β-subunit of thyrotropin (TSHB). The TSHB mutation C105Vfs114X leads to isolated thyroid-stimulating-hormone-(TSH)-deficiency and results in a severe phenotype. The aim of this study was to gain more insight into the underlying molecular mechanism and the functional effects of this mutation based on two assumptions: a) the three-dimensional (3D) structure of TSH should be modified with the C105V substitution, and/or b) whether the C-terminal modifications lead to signaling differences. 2) Methods: wild-type (WT) and different mutants of hTSH were generated in human embryonic kidney 293 cells (HEK293 cells) and TSH preparations were used to stimulate thyrotropin receptor (TSHR) stably transfected into follicular thyroid cancer cells (FTC133-TSHR cells) and transiently transfected into HEK293 cells. Functional characterization was performed by determination of Gs, mitogen activated protein kinase (MAPK) and Gq/11 activation. 3) Results: The patient mutation C105Vfs114X and further designed TSH mutants diminished cyclic adenosine monophosphate (cAMP) signaling activity. Surprisingly, MAPK signaling for all mutants was comparable to WT, while none of the mutants induced PLC activation. 4) Conclusion: We characterized the patient mutation C105Vfs114X concerning different signaling pathways. We identified a strong decrease of cAMP signaling induction and speculate that this could, in combination with diverse signaling regarding the other pathways, accounting for the patient's severe phenotype

Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment

The tremendous rise of generative AI has reached every part of society - including the news environment. There are many concerns about the individual and societal impact of the increasing use of generative AI, including issues such as disinformation and misinformation, discrimination, and the promotion of social tensions. However, research on anticipating the impact of generative AI is still in its infancy and mostly limited to the views of technology developers and/or researchers. In this paper, we aim to broaden the perspective and capture the expectations of three stakeholder groups (news consumers; technology developers; content creators) about the potential negative impacts of generative AI, as well as mitigation strategies to address these. Methodologically, we apply scenario writing and use participatory foresight in the context of a survey (n=119) to delve into cognitively diverse imaginations of the future. We qualitatively analyze the scenarios using thematic analysis to systematically map potential impacts of generative AI on the news environment, potential mitigation strategies, and the role of stakeholders in causing and mitigating these impacts. In addition, we measure respondents' opinions on a specific mitigation strategy, namely transparency obligations as suggested in Article 52 of the draft EU AI Act. We compare the results across different stakeholder groups and elaborate on the (non-) presence of different expected impacts across these groups. We conclude by discussing the usefulness of scenario-writing and participatory foresight as a toolbox for generative AI impact assessment

Artificial intelligence ethics by design:Evaluating public perception on the importance of ethical design principles of artificial intelligence

Despite the immense societal importance of ethically designing artificial intelligence (AI), little research on the public perceptions of ethical AI principles exists. This becomes even more striking when considering that ethical AI development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles (explainability, fairness, security, accountability, accuracy, privacy, machine autonomy) are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even impossible, as developers must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case - the use of AI in tax fraud detection. The results of a large conjoint survey (n=1099) suggest that, by and large, German respondents found the ethical principles equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred attributes, but also in the importance level of the attributes themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on AI. Societal implications as well as design challenges are discussed