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This paper provides an empirical ethics analysis of the goods and bads enacted in EU ageing and innovation policy discourses. It revolves around a case study of the persona Maria, developed as part of the EU’s Active and Healthy Ageing Policies. Drawing on Pols’ empirical ethics as a theoretical and methodological approach, we describe the variety of goods (practices/situations to be strived for) and bads (practices/situations to be avoided) that are articulated in Maria’s persona. We analyse how certain ideas about good and bad ageing—those associated with the use of sophisticated technologies—come to matter more in the solutions proposed for Maria and the framing of her unmet needs, while others which were initially seen as relevant and that describe her dreams, fears and interactions, are marginalised. The paper adds to existing studies of ageing and technology by analysing specific practices that render visible how the idea of technology and data sharing as evidently the right path towards futures of (good) ageing, comes to prevail.

The new radiotherapy high field, 1.5 Tesla MRI-guided linear accelerator (MR-Linac) is being clinically introduced. Sensing and evaluating opportunities and barriers at an early stage will facilitate its eventual scale-up. This study investigates the opportunities and barriers to the implementation of MR-Linac into prostate cancer care based on 43 semi-structured interviews with Dutch oncology care professionals, hospital and division directors, patients, payers and industry. The analysis was guided by the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability framework of new medical technologies and services. Opportunities included: the acquirement of (1) advanced MRI-guided radiotherapy technology with (2) the potential for improved patient outcomes and (3) economic benefits, as well as (4) professional development and (5) a higher hospital quality profile. Barriers included: (1) technical complexities, (2) substantial staffing and structural investments, (3) the current lack of empirical evidence of clinical benefits, (4) professional silos, and (5) the presence of patient referral patterns. While our study confirms the expected technical and clinical prospects from the literature, it also reveals economic, organizational, and socio-political challenges.

Simple Summary The value of animal research has been increasingly debated, owing to high failure rates in drug development. A potential contributor to this situation is the lack of predictivity of animal models of disease. Until recently, most initiatives focused on well-known problems related to the lack of reproducibility and poor reporting standards of animal studies. Only now, more methodologies are becoming available to evaluate how translatable the data from animal models of disease are. We discuss the use and implications of several methods and tools to assess the generalisability of animal data to humans. To analyse the relevance of animal research objectively, we must (1) guarantee the experiments are conducted and reported according to best practices; (2) ensure the selection of animal models is made with a clear and detailed translational rationale behind it. Once these conditions are met, the true value of the use of animals in drug development can be finally attested. Abstract Reports of a reproducibility crisis combined with a high attrition rate in the pharmaceutical industry have put animal research increasingly under scrutiny in the past decade. Many researchers and the general public now question whether there is still a justification for conducting animal studies. While criticism of the current modus operandi in preclinical research is certainly warranted, the data on which these discussions are based are often unreliable. Several initiatives to address the internal validity and reporting quality of animal studies (e.g., Animals in Research: Reporting In Vivo Experiments (ARRIVE) and Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE) guidelines) have been introduced but seldom implemented. As for external validity, progress has been virtually absent. Nonetheless, the selection of optimal animal models of disease may prevent the conducting of clinical trials, based on unreliable preclinical data. Here, we discuss three contributions to tackle the evaluation of the predictive value of animal models of disease themselves. First, we developed the Framework to Identify Models of Disease (FIMD), the first step to standardise the assessment, validation and comparison of disease models. FIMD allows the identification of which aspects of the human disease are replicated in the animals, facilitating the selection of disease models more likely to predict human response. Second, we show an example of how systematic reviews and meta-analyses can provide another strategy to discriminate between disease models quantitatively. Third, we explore whether external validity is a factor in animal model selection in the Investigator’s Brochure (IB), and we use the IB-derisk tool to integrate preclinical pharmacokinetic and pharmacodynamic data in early clinical development. Through these contributions, we show how we can address external validity to evaluate the translatability and scientific value of animal models in drug development. However, while these methods have potential, it is the extent of their adoption by the scientific community that will define their impact. By promoting and adopting high quality study design and reporting, as well as a thorough assessment of the translatability of drug efficacy of animal models of disease, we will have robust data to challenge and improve the current animal research paradigm.

Background Literature on self-management innovations has studied their characteristics and position in healthcare systems. However, less attention has been paid to factors that contribute to successful implementation. This paper aims to answer the question: which factors play a role in a successful implementation of self-management health innovations? Methods We conducted a narrative review of academic literature to explore factors related to successful implementation of self-management health innovations. We further investigated the factors in a qualitative multiple case study to analyse their role in implementation success. Data were collected from nine self-management health projects in the Netherlands. Results Nine factors were found in the literature that foster the implementation of self-management health innovations: 1) involvement of end-users, 2) involvement of local and business partners, 3) involvement of stakeholders within the larger system, 4) tailoring of the innovation, 5) utilisation of multiple disciplines, 6) feedback on effectiveness, 7) availability of a feasible business model, 8) adaption to organisational changes, and 9) anticipation of changes required in the healthcare system. In the case studies, on average six of these factors could be identified. Three projects achieved a successful implementation of a self-management health innovation, but only in one case were all factors present. Conclusions For successful implementation of self-management health innovation projects, the factors identified in the literature are neither necessary nor sufficient. Therefore, it might be insightful to study how successful implementation works instead of solely focusing on the factors that could be helpful in this process.

The future of food is one of the major world-wide challenges. In this perspective paper, we set-up a framework for a multi-disciplinary future food systems approach, building on the UN Sustainable Development Goals. We hereby combine a truly sustainable approach including social science aspects combined with the One Health approach. Scientists from a large number of backgrounds have addressed four key areas that are discussed in more detail in this paper: (i) nature inspired food production, (ii) sustainable immune resilience, (iii) social and cultural change of food behavior, and (iv) food fairness. We believe that transformations to integrated future food system approaches should move beyond single solutions and can only be solved by working in transdisciplinary settings of science, society, and industry.

Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. Key Points • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.

Poor translation of efficacy data derived from animal models can lead to clinical trials unlikely to benefit patients-or even put them at risk-and is a potential contributor to costly and unnecessary attrition in drug development.To develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy.We performed a scoping review to identify the key aspects used to validate animal models. Eight domains (Epidemiology, Symptomatology and Natural History-SNH, Genetic, Biochemistry, Aetiology, Histology, Pharmacology and Endpoints) were identified. We drafted questions to evaluate the different facets of human disease simulation. We designed the Framework to Identify Models of Disease (FIMD) to include standardised instructions, a weighting and scoring system to compare models as well as factors to help interpret model similarity and evidence uncertainty. We also added a reporting quality and risk of bias assessment of drug intervention studies in the Pharmacological Validation domain. A web-based survey was conducted with experts from different stakeholders to gather input on the framework. We conducted a pilot study of the validation in two models for Type 2 Diabetes (T2D)-the ZDF rat and db/db mouse. Finally, we present a full validation and comparison of two animal models for Duchenne Muscular Dystrophy (DMD): the mdx mouse and GRMD dog. We show that there are significant differences between the mdx mouse and the GRMD dog, the latter mimicking the human epidemiological, SNH, and histological aspects to a greater extent than the mouse despite the overall lack of published data.FIMD facilitates drug development by serving as the basis to select the most relevant model that can provide meaningful data and is more likely to generate translatable results to progress drug candidates to the clinic.