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This article addresses the need to establish a comprehensive conceptual framework for analysing healthcare systems and their transformations. It begins by offering an overview of the current state of the art in the field, pointing to the literature's absence of conceptual robustness in the definition of system types. By exploring the dimensions 'financing', 'provision' and 'regulation' of healthcare, the article then proceeds deductively in line with the 'Weberian method of ideal-types' to establish a taxonomy of 27 healthcare systems, of which three can be identified as 'ideal-types'. When applying this concept, not only can differences between healthcare systems be analysed, but also changes over time. The article concludes by identifying three forms of healthcare system transformation.

Background: The existence, usage and benefits of digital technologies in nursing care are relevant topics in the light of the current discussion on technologies as possible solutions to problems such as the shortage of skilled workers and the increasing demand for long-term care. A lack of good empirical overviews of existing technologies in the present literature prompted us to conduct this review. Its purpose was to map the field of digital technologies for informal and formal care that have already been explored in terms of acceptance, effectiveness and efficiency (AEE), and to show the scope of the used methods, target settings, target groups and fields of support. Methods: A systematic literature search was conducted using Medline, Scopus, CINAHL, Cochrane Library, ACM Digital Library, IEEE Xplore, the Collection of Computer Science Bibliographies, GeroLit and CareLit. In addition, project websites were manually screened for relevant publications. Results: Seven hundred fifteen papers were included in the review. Effectiveness studies have been most frequently performed for ICT, robots and sensors. Acceptance studies often focussed on ICT, robots and EHR/EMR. Efficiency studies were generally rare. Many studies were found to have a low level of evidence. Experimental designs with small numbers and without control groups were the most common methods used to evaluate acceptance and effectiveness. Study designs with high evidence levels were most commonly found for ICT, robots and e-learning. Technologies evaluated for informal caregivers and children or indicated for formal care at home or in cross-sectoral care were rare. Conclusion: We recommend producing high-quality evaluations on existing digital technologies for AEE in real-life settings rather than systematic reviews with low-quality studies. More focus should be placed on research into efficiency. Future research should be devoted to a closer examination of the applied AEE evaluation methods. Policymakers should provide funding to enable large-scale, long-term evaluations of technologies in the practice of care, filling the research gaps for technologies, target settings and target groups identified in this review.

This paper reports findings from a European Commission funded study of future long-term care expenditure in Germany, Italy, Spain and the United Kingdom, and presents projections of future long-term care expenditure in the four countries under a number of assumptions about the future. Macro-simulation (or cell-based) models were used to make comparable projections based on a set of common assumptions. A central base-case served as a point of comparison by which to explore the sensitivity of the models to alternative scenarios for the key determinants. The sensitivity of the models to variant assumptions about the future numbers of older people, the prevalence of functional dependency and informal care, patterns of long-term provision, and macroeconomic conditions are examined. It was found that, under the base-case, the proportion of gross domestic product spent on long-term care is projected to more than double between 2000 and 2050 in each country. The projected future demand for longterm care services for older people is sensitive to assumptions about the future number of older people, the prevalence of dependency and the availability of informal care, and projected expenditure is sensitive to assumptions about rises in the real unit-costs of services and the structure of the models. It is important, for planning purposes, to recognise the considerable uncertainty about future levels of long-term care expenditure.KEY WORDS -Long-term care expenditure, demand for long-term care, projections.

After fifteen years of existence, Germany's long-term care insurance shows both successes and weaknesses. The latter led to the 2008 reform, which concentrated on quality improvements, care management and careful adjustments of benefits. While attempts to improve quality and care management contain promising elements, new rules for adjustment are disappointing. This is also true for the issue of future financing as the modest increase in the contribution rate, which is part of the reform, only buys time. Thus, the next round of reform is already in the making, marking the scheme as a system of permanent reform. As Germany is one of the most clear-cut examples of social insurance, the assessment of this scheme and its recent reform also allow us to draw some general lessons for the design of long-term care social insurance schemes.