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Innovation in a decentralized blockchain infrastructure can be used by medicine as a prerequisite for the exchange of patient data. Developments in the medical device industry that support the technology of the internet of things and wireless sensor networks also facilitate the examination of patient medical records that no longer require visits to the practice of doctors or hospitals which in some cases takes in a considerable time. Not to mention the consideration of traffic congestion and busy routine in the work. Patients can check their healthcare concerns using only sensors such as e-Health Sensor Shield Platform which then sends recording results through the transmission line to the data lakes. However, this patient’s medical record data is very confidential and may only be accessed by certain parties only. This required the design of the concept of security in the transmission of data so that the data does not leak to parties who are not eligible. This paper attempts to provide an overview of the concept of using encryption with an asymmetric key for securing data from sensors to data lakes before forwarding to a decentralized, interconnected blockchain infrastructure.

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 concludes the book on cross-innovation between audiovisual media industries and three other sectors – education, health care and tourism. It emphasises, first, the importance of platformisation as a socio-economic and technological process in framing all cross-innovation processes. It highlights how the rather full platformisation of tourism has negatively affected the interest of the tourism industry small and medium-sized enterprises to cooperate with local media and gaming industries in search of new solutions. Relatedly it proposes a generic conflict between platformisation of specific fields and the health of thematic local cross-innovation systems involving media and creative sectors. It then discusses that the inherent fragmentation of the health and education sectors has not allowed their international platformisation, but constitutes challenges to innovators interested in international scalability. It also discusses the reasons why two publicly coordinated cross-innovation processes – one involving the use of virtual reality in health care and another using augmented reality – have given different results – one a relative success and the other not as of yet. At the end of the chapter final definitions of cross-innovation are offered and the operationalisation of the term and the associated conceptual approach are assessed.

This chapter concludes the section on cross-innovation practices between audiovisual (AV) media industries and the health care sector. It suggests that the main case studies discussed in this section – Estonia in general and Aarhus Region in Denmark – tell of two different trajectories on how the emergence of cross-innovation systems can be facilitated by policies. Local policymakers in Aarhus have worked systematically to raise awareness and facilitate contacts between AV media and other sectors and this has resulted in an active start-up scene at the intersection between the media and the health care industries. Estonia, which is focusing on traditional cultural policymaking, has not recognised similar dynamics. Yet, Estonia may be still better prepared for the (global) platformisation of e-health services with its national e-governance systems, while Denmark’s health-related e-services remain fragmented and ripe for platformisation by multinationals, potentially undermining local cross-innovation systems.

In order to assess the state of health of Europe’s children, or to appraise the systems and models of healthcare delivery, data about children are essential, with as much precision and accuracy as possible by small group characteristic. Unfortunately, the experience of the Models of Child Health Appraised (MOCHA) project and its scientists shows that this ideal is seldom met, and thus the accuracy of appraisal or planning work is compromised. In the project, we explored the data collected on children by a number of databases used in Europe and globally, to find that although the four quinquennial age bands are common, it is impossible to represent children aged 0–17 years as a legally defined group in statistical analysis. Adolescents, in particular, are the most invisible age group despite this being a time of life when they are rapidly changing and facing increasing challenges. In terms of measurement and monitoring, there is little progress from work of nearly two decades ago that recommended an information system, and no focus on the creation of a policy and ethical framework to allow collaborative analysis of the rich anonymised databases that hold real-world people-based data. In respect of data systems and surveillance, nearly all systems in European society pay lip service to the importance of children, but do not accommodate them in a practical and statistical sense.

Information and communication technologies can transform how services can be and are delivered as has already happened in other arenas, such as civil aviation, financial services and retailing. Most modern health care is heavily dependent on e-health, including record keeping, targeted information sharing and digital diagnostic and imaging techniques. However, there remains little scientific knowledge base for optimal system content and function in primary health care, particularly for children. Models of Child Health Appraised (MOCHA) aimed to establish the current e-health situation in children’s primary care services. Electronic health records (EHRs) are in regular use in much of northern and western Europe and in some newer European Union Member States, but other countries lag behind. MOCHA investigated the use of unique identifiers, the use of case-based public health EHRs and the capability of record linkage, linkage of information with school health data and monitoring of social media influences, such as health websites and health apps. A widespread lack of standards underlined a lack of research enquiry into this issue in terms of children’s health data and health knowledge. Health websites and apps are a growing area of healthcare delivery, but there is a worrying lack of safeguards in place. The challenge for policy-makers and practitioners is to be aware and to lead on the innovative harnessing of new technologies, while protecting child users against new harms.

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.

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.

A policy of surveillance which interferes with the fundamental right to a private life requires credible justification and a supportive evidence base. The authority for such interference should be clearly detailed in law, overseen by a transparent process and not left to the vagaries of administrative discretion. If a state surveils those it governs and claims the interference to be in the public interest, then the evidence base on which that claim stands and the operative conception of public interest should be subject to critical examination. Unfortunately, there is an inconsistency in the regulatory burden associated with access to confidential patient information for non-health-related surveillance purposes and access for health-related surveillance or research purposes. This inconsistency represents a systemic weakness to inform or challenge an evidence-based policy of non-health-related surveillance. This inconsistency is unjustified and undermines the qualities recognised to be necessary to maintain a trustworthy confidential public health service. Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to constrain the arbitrary or unwarranted exercise of power through judicial review is not sufficient to level the playing field. The authors recommend ‘levelling up’ in procedural oversight, and adopting independent mechanisms equivalent to those adopted for establishing the operative conceptions of public interest in the context of health research to non-health-related surveillance purposes.

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.