This chapter focuses on causal inference in healthcare, emphasizing the need to identify causal relationships in data to answer important questions related to efficacy, mortality, productivity, and care delivery models. The authors discuss the limitations of randomized controlled trials due to ethical or pragmatic considerations and introduce quasi-experimental research designs as a scientifically coherent alternative. They divide these designs into two broad categories, independence-based designs and model-based designs, and explain the validity of assumptions necessary for each design. The chapter covers key concepts such as potential outcomes, selection bias, heterogeneous treatment effects bias, average treatment effect, average treatment effect for the treated and untreated, and local average treatment effect. Additionally, it discusses important quasi-experimental designs such as regression discontinuity, difference-in-differences, and synthetic controls. The chapter concludes by highlighting the importance of careful selection and application of these methods to estimate causal effects accurately and open the black box of healthcare.

The goal of this chapter is to introduce the reader to the wide range of methods used for performance analysis in healthcare. Specifically, it starts with a brief outline of what the authors refer to as the basic analytics of healthcare performance analysis, with an emphasis on hospitals. Although relatively simple, such basic performance analytics are popular approaches in practice: They are useful for exploring and presenting the data before proceeding with more sophisticated methods, and they provide a bridge for communication between practitioners and academics. To facilitate the discussion, they authors provide brief empirical illustrations using real data sets as well as supply the relevant R codes of the examples. They then briefly describe other major approaches for performance analysis in healthcare as a primer for the following chapters.

The healthcare sector not only plays a key role in a country’s economy but is also one of the fastest growing sectors for most countries, resulting in rising expenditures. In turn, efficiency and productivity analyses of the healthcare industry have attracted attention from a wide variety of interested parties, including academics, hospital administrators, and policy makers. As a result, a large number of studies on efficiency and productivity in the healthcare industry have appeared over the past four decades in a variety of outlets. This chapter presents a performance analysis and science mapping of these studies with the aid of modern machine technology learning methods for bibliometric analysis. This approach revealed patterns and clusters in the data from 1,059 efficiency and productivity articles associated with the healthcare industry produced by nearly 2,300 authors and published in a multitude of Scopus-indexed academic journals from 1983 to 2021. Leveraging such biblioanalytics, which are combined with our own understanding of the field, the authors highlight the trends and possible future of studies on efficiency and productivity in healthcare.

Facing the challenges of aging populations, new technologies provide a potential solution to meeting the increasing needs associated with demographic changes by increasing productivity in healthcare production. However, decision-makers require evidence of whether the adoption of new technologies improves the efficiency of healthcare resource use. Cost-effectiveness analysis (CEA) is a methodology for evaluating new technologies by comparing a new intervention with the current intervention (or mix of different interventions) used for treating the same patient group. This chapter explores the theoretical foundations of CEA and the conditions required for CEA to inform decision-makers about the efficiency of implementing the new intervention are identified. The implications of using CEA as a basis for decision-making in the absence of these theoretical conditions are discussed, and solutions to addressing the efficiency problems under real-world conditions are derived. Where practical considerations limit the ability of decision-makers to apply these solutions, an alternate practical approach, focused on efficiency improvements as opposed to efficiency maximization, is presented.

The goal of this chapter is to sketch out a theoretical frontier model capable of estimating the production of well-being from healthcare interventions. The model is multidimensional in nature and takes a starting point in the utilization of healthcare interventions to produce a change in disease severity. The model allows for many healthcare interventions, such as doctor’s visits and pharmaceuticals, to be used as inputs in the production of improvements in disease severity assessed with different clinical outcome measures. Moreover, the model does not end with the production of clinical improvements but continues with estimating how the change in disease severity affects the individual’s ability to maximize well-being. Data from the Swedish National Register for Systemic Treatment of Psoriasis (PsoReg) are utilized to illustrate the model.

This chapter proposes a framework for estimating the investment in human capital from health improvement or activities that improve life expectancy and reduce morbidity rates. The measurement framework builds on and extends the Jorgenson-Fraumeni income-based approach for estimating human capital to account for the effect of health on human capital. This economic approach to measuring health human capital differs from the welfare-based approach that estimates the economic effect of health improvements on the quality of life and well-being of individuals. The framework is then implemented for Canada, and the investment in health human capital for the period from 1970 to 2020 is estimated. The estimated investment in health human capital based on the income approach was found to be lower than health expenditures in Canada. This suggests that much of the health expenditures should be classified as consumption rather than as an investment that increases earnings.

Health system spending, and the consequent impact on health are increasingly a focus of governments around the world. Given the strain on resources and systems, increasingly scarce resources require targeting more effectively. Measuring efficiency and productivity are increasingly the focus of government gepartments, both nationally and locally. Thus, assessing how efficiency is measured and how valid and robust results are is critical to those involved in policy and service delivery. This chapter presents revised guidelines as to how users should set up such studies to be as useful as possible and how end users can assess how useful they actually are to them in their specific setting. Conclusions are drawn as to how these can be used in a fast-changing world, and potential consequences of not following guidance are discussed.

This chapter provides a brief overview of the stochastic frontier analysis (SFA) in the context of analyzing healthcare, with a focus on hospitals, where it has received widespread attention. The authors consider many of the popular extensions and generalizations of the classic SFA model in both cross-sectional and panel data. They also briefly discuss semiparametric and nonparametric generalizations, spatial frontiers, Bayesian SFA, and the endogeneity in SFA. They illustrate some of these methods for real data on public hospitals in Queensland, Australia, as well as provide practical guidance and references for their computational implementations via R.

This chapter discusses the implications of considering the health system as part of the national social infrastructure, using the example of the demand surge experienced during the COVID-19 pandemic for the UK’s rationed, largely free at the point of need National Health Service (NHS). It explores the impact of the past prioritization of the cost efficiency of the service rather than long-term need. An infrastructure perspective incorporating resilience and peak-demand considerations sheds new light on the performance of the health service and underlines the role of healthcare systems in human capital investment and economy-wide productivity.

For millennia, health and disease have shaped human society in profound and fundamental ways. While events such as the Justinian Plague and ‘Black Death’ decimated the European populations in the sixth and fourteenth centuries respectively, arresting urban development and impacting the relationship between church and state, the introduction of European and African diseases into Latin America is believed to have caused the deaths of up to 90 per cent of some of the continent’s indigenous populations. Biological weapons used during World War I led to international moratoriums on their use, even as more recent ‘naturally occurring’ events extending from the 2003 SARS outbreak, the 2013–16 West African Ebola outbreak and the COVID-19 pandemic have had widespread social, economic and political impacts.

In Chapter 13, we provide a preliminary analysis of the policy orientation of the EU’s post-Covid-19 new economic governance (NEG) regime to give policymakers, unionists, and social-movement activists an idea about possible future trajectories of EU governance of employment relations and public services. We do that on the basis of not only the recently adopted EU laws in these two policy areas, such as the decommodifying Minimum Wage Directive, but also EU executives’ post-Covid-19 NEG prescriptions in two areas (employment relations, public services), three public sectors (transport services, water services, healthcare services), and four countries (Germany, Italy, Ireland, Romania). Vertical NEG interventions in national wage policies paradoxically cleared the way for the decommodifying EU Minimum Wage Directive by effectively making wage policy an EU policymaking issue, but, in the area of public services, we see an accentuation of the trend of NEG prescriptions in recent years: more public investments but also much more private sector involvement in the delivery of public services.

The goal of Chapter 5 is to examine emoji use across the healthcare landscape, as well as what implications related to emoji theories can be gleaned from such usage and how emoji use can be applied to training healthcare professionals more generally. Prominently discussed in the chapter are clinical studies that indicate emoji writing (between practitioners and patients) may actually enhance medical outcomes. Also highlighted is the empirically attested fact that emoji scales and models may be good gauges for assessing well-being. The overall conclusion that can be drawn from the studies is that emoji might affect patients positively. Emoji are not medical cures in themselves, needless to say; they are simple pictures that affect patients positively, much like humor. They may also counteract the so-called nocebo effect, defined as a detrimental effect on health produced by psychological or psychosomatic factors such as negative expectations of treatment or prognosis.

Chapter 11 compares the policy orientation of the EU’s new economic governance (NEG) prescriptions in two policy areas (employment relations, public services), three sectors (transport, water, healthcare), four countries (Germany, Italy, Ireland, Romania) from 2009 to 2019. It reveals that almost all qualitative prescriptions pointed in a commodifying direction. Most quantitative prescriptions tasked governments to curtail wages and public expenditures too, but, over time, they not only became less coercive but also increasingly pointed in a decommodifying direction, tasking governments to invest more. It would, however, still be wrong to speak of a socialisation of NEG, not just given the decommodifying prescriptions’ weak coercive power but also because of their links to policy rationales that are compatible with NEG’s overarching commodification script. Moreover, Chapter 11 shows that NEG prescriptions tasked governments to channel more public resources into the allegedly more productive sectors (transport and water services) rather than into essential social services like healthcare. Given NEG’s country-specific methodology, it is not surprising that there have been only few instances of transnational action on specific NEG prescriptions. By contrast, the share of transnational labour protests targeting EU interventions broadly defined increased after 2008. This suggests that NEG has been altering protest landscapes.

Chapter 7 distills from the empirical studies their implications for emoji theories overall and for their applicability to the educational and healthcare realms. The studies have borne a number of concrete implications for emoji theory in general, including how they fit in with communication theories, including nonverbal aspects. Several theoretical notions are developed as well, generalizing them from previous chapters, including the apparent function of emoji as “annotators” of meaning, not just conveyors of prosodic or gestural features in writing. Another notion is that of episodic meaning, whereby the placement of an emoji in the episodes that constitute a message adds to it semiotically. Emoji grammar is thus more appropriately characterized as an episodic grammar.

Chapter 10 traces the EU governance of health services and its discontents. The first European interventions in the health sector facilitated mobile workers’ access to health services in their host countries, thereby decommodifying cross-border care, albeit by recourse to solidaristic mechanisms situated at national rather than EU level. Since the 1990s however, European horizontal market pressures and EU public deficit criteria have led governments to curtail healthcare spending and to introduce marketising reforms. Thereafter, healthcare became a target of EU competition and free movement of services law. In 2006, transnational collective action of trade unions and social movements moved EU legislators to drop healthcare from the scope of the draft EU Services Directive. After the financial crisis of 2008 however, EU executives pursued commodification of healthcare through new means, as shown by our analysis of their new economic governance (NEG) prescriptions for Germany, Italy, Ireland, and Romania. Even when commodifying prescriptions were on occasion accompanied by decommodifying ones, the latter remained subordinated to the former. Although NEG’s country-specific methodology hampered transnational protests, the overarching commodification script of NEG prescriptions led not only to transnational protests by the European Federation of Public Service Unions, but also to the formation of the European Network against the Privatisation and Commercialisation of Health and Social Protection, which unites unionists and social-movement activists.

The historical relationship between semiotics and healthcare is explored in Chapter 3. The authors look specifically at the link between education and healthcare communications that is established by the use of emoji in such communications. The semioliterate nature of healthcare and its implications for respective education are explored, particularly as these relate to early diagnoses based on physical signs and symptoms. Parallels are then drawn between the semioliterate qualities of emoji in the Petcoff study (Chapter 2) and the potentiality of emoji as an effective doctor-to-patient healthcare communicative tool. The chapter concludes by considering how the emoji code can be inserted into traditional healthcare professional education settings, so as to show students how effective it can be in practitioner–patient interactions.