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There is considerable heterogeneity between primary care systems that have evolved in individual national cultural environments. Models of Child Health Appraised (MOCHA) studied how the transfer of models or their individual components can be achieved across nations, using examples of combinations of settings, functions, target groups and tracer conditions. There are many factors that determine the feasibility of successful transfer of these from one setting to another, which must be recognised and taken into account. These include the environment of the care system, national policy-making and contextual means of directing population behaviour – in the form of penalties and incentives, which cannot be assessed or expected to work by means of rational actions alone. MOCHA developed a list of criteria to assess transferability, summarised in a population characteristics, intervention content, environment and transfer (PIET-T) process. To explore the process and means of transferability, we obtained consensus statements from the researchers on optimum model scenarios and conducted a survey of stakeholders, professionals and users of children’s primary care services that involved three specific health topics: vaccination coverage in infants, monitoring of a chronic or complex condition and early recognition of mental health problems. The results give insight into features of transferability – such as the availability and the use of guidelines and formal procedures; the barriers and facilitators of implementation and similarities and differences between model practices and the existing model of child primary care in the country. We found that successful transfer of an optimal model is impossible without tailoring the model to a specific country setting. It is vital to be aware of the sensitivity of the population and environmental characteristics of a country before starting to change the system of primary care.

The Models of Child Health Appraised (MOCHA) project identified the different models of primary care that exist for children, examined the particular attributes that might be different from those directed at adults and considered how these models might be appraised. The project took the multiple and interrelated dimensions of primary care and simplified them into a conceptual framework for appraisal. A general description of the models in existence in all 30 countries of the EU and EEA countries, focusing on lead practitioner, financial and regulatory and service provision classifications, was created. We then used the WHO ‘building blocks’ for high-performing health systems as a starting point for identifying a good system for children. The building blocks encompass safe and good quality services from an educated and empowered workforce, providing good data systems, access to all necessary medical products, prevention and treatments, and a service that is adequately financed and well led. An extensive search of the literature failed to identify a suitable appraisal framework for MOCHA, because none of the frameworks focused on child primary care in its own right. This led the research team to devise an alternative conceptualisation, at the heart of which is the core theme of child centricity and ecology, and the need to focus on delivery to the child through the life course. The MOCHA model also focuses on the primary care team and the societal and environmental context of the primary care system.

This chapter summarises the role of EU actions in supporting healthcare policies in the EU Member States, both looking at implemented actions and describing current priorities for the future. It argues that these coordinated actions can be beneficial for EU Member States by helping them to avoid duplication of effort and to attain economies of scale. Moreover, data sharing with proper safeguards can unleash vast amount of ‘learning what works’ both for medical treatments and for healthcare sustainability measures. The need for this common learning appears ever more urgent while facing the health and economic consequences of the present pandemic.

There is more to primary care than solely medical and nursing services. Models of Child Health Appraised (MOCHA) explored the role of the professions of pharmacy, dental health and social care as examples of affiliate contributors to primary care in providing health advice and treatment to children and young people. Pharmacies are much used, but their value as a resource for children seems to be insufficiently recognised in most European Union (EU) and European Economic Area (EEA) countries. Advice from a pharmacist is invaluable, particularly because many medicines for children are only available off-label, or not available in the correct dose, access to a pharmacist for simple queries around certain health issues is often easier and quicker than access to a primary care physician or nursing service. Preventive dentistry is available throughout the EU and EEA, but there are few targeted incentives to ensure all children receive the service, and accessibility to dental treatment is variable, particularly for disabled children or those with specific health needs. Social care services are an essential part of health care for many extremely vulnerable children, for example those with complex care needs. Mapping social care services and the interaction with health services is challenging due to their fragmented provision and the variability of access across the EU and EEA. A lack of coherent structure of the health and social care interface requires parents or other family members to navigate complex systems with little assistance. The needs of pharmacy, dentistry and social care are varied and interwoven with needs from each other and from the healthcare system. Yet, because this inter-connectivity is not sufficiently recognised in the EU and EEA countries, there is a need for improvement of coordination and with the need for these services to focus more fully on children and young people.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation – artificial intelligence (AI) – for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

It’s a global challenge to make cities and communities become an age-friendly society. This paper aims to discuss how to develop good concepts for senior residences in Norway and aim to study what the challenges are in the early planning phase, searching the answer to the following research questions: (1) What makes a senior housing attractive? (2) What are the challenges that hinder future concept development? (3) Suggest actions in order to obtain a sustainable development.

This research uses a descriptive and explorative approach describing the phenomena by (I) a short literature review describing existing concepts and challenges, (II) “Walk-through”-methodology with informal dialogue on site and (II) semi-structured interviews of property developers, architects or contractors, politicians, care providers or planners in the municipality involved in seven pilot projects in Kristiansand and Stavanger.

The authors find that new and diverse concepts need to be developed to meet the demand of the seniors. The new concepts should be developed in collaboration with both public and private actors as well as developing a communication platform to meet the needs of the seniors in terms understanding the possibilities of alternative housings, incentives to move and how to influence and get involved in the planning.

There is a limited no. of informants among the public stakeholders. Only three of the seven pilot projects are accomplished. There is an advantage if the rest of the projects are evaluated when accomplished.

Develop participation models and PPP models at the local level.

The value lies in the evaluation of the seven pilot projects.

The 30 MOCHA (Models of Child Health Appraised) countries are diverse socially, culturally and economically, and differences exist in their healthcare systems and in the scope and role of primary care. An economic analysis was undertaken that sought to explain differences in child health outcomes between countries. The conceptual framework was that of a production function for health, whereby health outputs (or outcomes) are assumed affected by several ‘inputs’. In the case of health, inputs include personal (genes, health behaviours) and socio-economic (income, living standards) factors and the structure, organisation and workforce of the healthcare system. Random effects regression modelling was used, based on countries as the unit of analysis, with data from 2004 to 2016 from international sources and published categorisations of healthcare system. The chapter describes the data deficiencies and measurement conundrums faced, and how these were addressed. In the absence of consistent indicators of child health outcomes across countries, five mortality measures were used: neonatal, infant, under five years, diabetes (0–19 years) and epilepsy (0–19 years). Factors found associated with reductions in mortality were as follows: gross domestic product per capita growth (neonatal, infant, under five years), higher density of paediatricians (neonatal, infant, under five years), less out-of-pocket expenditure (neonatal, diabetes 0–19), state-based service provision (epilepsy 0–19) and lower proportions of children in the population, a proxy for family size (all outcomes). Findings should be interpreted with caution due to the ecological nature of the analysis and the limitations presented by the data and measures employed.

This chapter discusses how ‘hidden’ populations of women who use drugs (WWUD) in coastal Kenya became ‘visible’ through accessing health, harm reduction and rights services. This effort was facilitated by the Muslim Education and Welfare Association (MEWA) and their work with community leaders. Mapping undertaken by MEWA outreach workers identified women who use opioids and other substances in isolated drug use settings. MEWA introduced daily meals in identified sites, needle and syringe services and residential rehabilitation services for opioid withdrawal. The introduction of residential adherence services for HIV and tuberculosis and the provision of methadone enabled compliance with the 90-90-90 UN HIV cascade. These services achieved sustained HIV viral load suppression at 83% and a 100% cure rate for tuberculosis among WWUD. In addition, disclosure and partner risk tracing was established in the ‘drug dens’ for women who test positive for STIs. The introduction of women-only hours once per week at the drop-in centre contributed to a better understanding of drug dependency among outreach workers and clinicians. Policies on gender-based violence were also refined, leading to an increase in reported cases. Programmes targeting families were introduced to promote effective communication and improve parenting skills. Access barriers to social security programmes were tackled by a network of paralegal officers in partnership with Kenyan authorities. Finally, entrepreneurship training and mentorship programmes were implemented to build resilience among WWUD.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.