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This study explores the evaluation of research pathways of self-management health innovations from discovery to implementation in the context of practice-based research. The aim is to understand how a new process model for evaluating practice-based research provides insights into the implementation success of innovations. Data were collected from nine research projects in the Netherlands. Through document analysis and semi-structured interviews, we analysed how the projects start, evolve, and contribute to the healthcare practice. Building on previous research evaluation approaches to monitor knowledge utilization, we developed a Research Pathway Model. The model’s process character enables us to include and evaluate the incremental work required throughout the lifespan of an innovation project and it helps to foreground that innovation continues during implementation in real-life settings. We found that in each research project, pathways are followed that include activities to explore a new solution, deliver a prototype and contribute to theory. Only three projects explored the solution in real life and included activities to create the necessary changes for the solutions to be adopted. These three projects were associated with successful implementation. The exploration of the solution in a real-life environment in which users test a prototype in their own context seems to be a necessary research activity for the successful implementation of self-management health innovations.

Purpose: Magnetic resonance image (MRI)-guided radiation therapy with the 1.5 Tesla magnetic resonance linear accelerator (MR-Linac) is a rapidly evolving and emerging treatment. The MR-Linac literature mainly focused on clinical and technological factors in technology implementation, but it is relatively silent on health care system-related factors. Consequently, there is a lack of understanding of opportunities and barriers in implementing the MR-Linac from a health care system perspective. This study addresses this gap with a case study of the US health care system. Methods and Materials: An exploratory, qualitative research design was used. Data collection consisted of 23 semistructured interviews ranging from clinical experts at the radiation therapy and radiology department to insurance commissioners in 7 US hospitals. Analysis of opportunities and barriers was guided by the Nonadoption, Abandonment, Scale-up, Spread and Sustainability framework for new medical technologies in health care organizations. Results: Opportunities included high-precision MR-guidance during radiation therapy with potential continued technical advances and better patient outcomes. MR-Linac also offers opportunities for research, professional, and economic development. Barriers included the lack of empirical evidence of clinical effectiveness, technological complexity, and large staffing and structural investments. Furthermore, the presence of patients with disadvantaged socioeconomic background, and the lack of appropriate reimbursement as well as regulatory conditions can hinder technology implementation. Conclusions: Our study confirms the current literature on implementing the MR-Linac, but also reveals additional challenges for the US health care system. Alongside the well-known clinical and technical factors, also professional, socioeconomic, market, and governing influences affect technology implementation. These findings highlight new connections to facilitate technology uptake and provide a richer start to understanding its long-term effect.

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 Author(s) 2019., The rapidly growing and diversifying incubator population has led to increasing efforts to understand why entrepreneurs prefer one incubator over another. Scientific studies suggest that entrepreneurs should prefer incubators that provide startups with intangible resources, such as business knowledge or networks to enhance performance. Yet, studies show many entrepreneurs prefer incubators that provide tangible resources, such as funding and office space. The heterogeneity in preferences for resources from incubators is poorly understood. We do not know whether there are patterns in this heterogeneity nor what factors explain this heterogeneity. Thereby, we do not know the extent to which a one-size-fits-all model of incubation is sufficient to attract and support startups or whether incubators need to tailor themselves to the perceived resource needs of different groups of startups. Hence, this paper aims to identify and explain the heterogeneity in preferences for resources offered by an incubator to startups. We conducted a discrete choice experiment to determine how the attributes of an incubator influence incubator choice by different latent classes of entrepreneurs. The data comes from 935 entrepreneurs in North America and Western Europe. Our results reveal three latent classes of entrepreneurs: “ambitious, balanced spinoffs,” who consider all the incubator’s attributes when making a decision; “innovation-driven funding seekers,” who base their choice on funding availability; and “self-made individualists,” who disfavor networking, training, and coaching. The ambitious, balanced spinoffs class based their choice on the attributes highlighted in the literature, while the innovation-driven funding seekers and self-made individualists fit more with empirically observed preferences for tangible resources. The classes show that systematic heterogeneity exists in the preferences for resources provided by an incubator. We advise incubator managers how to better tailor their support to the preferences and needs of classes of startups., This work was supported by the Climate KIC program of the European Institute of Technology, and the Netherlands Organization for Scientifc research (NWO).

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.

National and international laws and regulations exist to protect animals used for scientific purposes in translational and applied research, which includes drug development. However, multiple animal models are available for each disease. We evaluated the argumentation behind the selection of a specific animal model using thematic content analysis in project applications issued in 2017-2019 in the Netherlands. In total, 125 animal models for translational and applied research from 110 project applications were assessed. Explanations to select a specific model included: the model’s availability (79%); the availability of expertise (62%); and the model showing similar disease pathology/symptoms (59%) to humans. Therefore, current selection of a specific animal model seems to be based on tradition rather than its potential predictive value for clinical outcome. The applicants’ explanations for the implementation of the 3R prin­ciples (replacement, reduction and refinement) as to the animal model were unspecific. Replacement was achieved by using data from prior in vitro studies, reduction by optimal experimental design and statistics, and refinement by reducing discomfort. Additionally, due to the stated need for a test model with high complexity (47%) and intactness (30%), the full replacement of animal models with alternative (non-live animal) approaches was thought unachievable. Without a clear, systematic and transparent justification for the selection of a specific animal model, the likelihood of poorly trans­latable research remains. It is not only up to the researcher to demonstrate this, as ethical committees and funding bodies can provide positive stimuli to drive this change.

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.