Suchergebnisse

Background and Objectives Common health state valuation methodology, such as time tradeoff (TTO) and standard gamble (SG), is typically applied under several descriptively invalid assumptions, for example, related to linear quality-adjusted life years (QALYs) or expected utility (EU) theory. Hence, the current use of results from health state valuation exercises may lead to biased QALY weights, which may in turn affect decisions based on economic evaluations using such weights. Methods have been proposed to correct responses for the biases associated with different health state valuation techniques. In this article we outline the relevance of prospect theory (PT), which has become the dominant descriptive alternative to EU, for health state valuations and economic evaluations. Methods and Results We provide an overview of work in this field, which aims to remove biases from QALY weights. We label this "the corrective approach." By quantifying PT parameters, such as loss aversion, probability weighting, and nonlinear utility, it may be possible to correct TTO and SG responses for biases in an attempt to produce more valid estimates of preferences for health states. Through straightforward examples, this article illustrates the effects of this corrective approach and discusses several unresolved issues that currently limit the relevance of corrected weights for policy. Conclusions Suggestions for research addressing these issues are provided. Nonetheless, if validly corrected health state valuations become available, we argue in favor of using these in economic evaluations.

The impact of digitalization of health services has been profound and is expected to be even more profound in the future. It is important to evaluate whether digital health services contribute to health system goals in an optimal way. This should be done at the level of the service, not the 'digital transformation'. Decisions to adopt new digital health services, at different levels of the health care system, are ideally based on evidence regarding their performance in light of health system goals. In order to evaluate this, a broad perspective should be taken in evaluations of digital health services. Attainment of the broad health system goals, including quality, efficiency and equity, are objectives against which to judge new digital health services. These goals in a broad sense are unaltered by the process of digitalization. Governance should be designed and tailored in such a way to capture all relevant changes in an adequate way. When evaluating digital health services many specific aspects need to be considered. Like for other innovations and (new) technologies, such promises may or may not materialize and potential benefits may also be accompanied by unintended and/or negative (side) effects in the short or long term. Hence, the introduction, implementation, use and funding of digital health technologies should be carefully evaluated and monitored. Governments should play a more active role in the further optimization both of the process of decision making (both at the central and decentral level) and the related outcomes. They need to find a balance between centralized and decentralized activity. Moreover, the broader preparation of the health care system to be able to deal with digitalization, from education, through financial and regulatory preconditions, to implementation of monitoring systems to monitor its effects on health system performance remains important.

To understand the public sentiment toward the measures used by policymakers for COVID-19 containment, a survey among representative samples of the population in seven European countries was carried out in the first two weeks of April 2020. The study addressed people's support for containment policies, worries about COVID-19 consequences, and trust in sources of information. Citizens were overall satisfied with their government's response to the pandemic; however, the extent of approval differed across countries and policy measures. A north-south divide in public opinion was noticeable across the European states. It was particularly pronounced for intrusive policy measures, such as mobile data use for movement tracking, economic concerns, and trust in the information from the national government. Considerable differences in people's attitudes were noticed within countries, especially across individual regions and age groups. The findings suggest that the epidemic acts as a stressor, causing health and economic anxieties even in households that were not directly affected by the virus. At the same time, the burden of stress was unequally distributed across regions and age groups. Based on the data collected, we draw lessons from the containment stage and identify several insights that can facilitate the design of lockdown exit strategies and future containment policies so that a high level of compliance can be expected.

Introduction The new European Union (EU) Regulations on medical devices and on in vitro diagnostics provide manufacturers and Notified Bodies with new tools to improve pre-market and post-market clinical evidence generation especially for high-risk products but fail to indicate what type of clinical evidence is appropriate at each stage of the whole lifecycle of medical devices. In this paper we address: i) the appropriate level and timing of clinical evidence throughout the lifecycle of high-risk implantable medical devices; and ii) how the clinical evidence generation ecosystem could be adapted to optimize patient access. Areas covered The European regulatory and health technology assessment (HTA) contexts are reviewed, in relation to the lifecycle of high-risk medical devices and clinical evidence generation recommended by international network or endorsed by regulatory and HTA agencies in different jurisdictions. Expert opinion Four stages are relevant for clinical evidence generation: i) pre-clinical, pre-market; ii) clinical, pre-market; iii) diffusion, post-market; and iv) obsolescence & replacement, post-market. Each stage has its own evaluation needs and specific studies are recommended to generate the appropriate evidence. Effective lifecycle planning requires anticipation of what evidence will be needed at each stage.

Introduction: The new European Union (EU) Regulations on medical devices and on in vitro diagnostics provide manufacturers and Notified Bodies with new tools to improve pre-market and post-market clinical evidence generation especially for high-risk products but fail to indicate what type of clinical evidence is appropriate at each stage of the whole lifecycle of medical devices. In this paper we address: i) the appropriate level and timing of clinical evidence throughout the lifecycle of high-risk implantable medical devices; and ii) how the clinical evidence generation ecosystem could be adapted to optimize patient access. Areas covered: The European regulatory and health technology assessment (HTA) contexts are reviewed, in relation to the lifecycle of high-risk medical devices and clinical evidence generation recommended by international network or endorsed by regulatory and HTA agencies in different jurisdictions. Expert opinion: Four stages are relevant for clinical evidence generation: i) pre-clinical, pre-market; ii) clinical, pre-market; iii) diffusion, post-market; and iv) obsolescence & replacement, post-market. Each stage has its own evaluation needs and specific studies are recommended to generate the appropriate evidence. Effective lifecycle planning requires anticipation of what evidence will be needed at each stage