The chapter examines the evidence on strategic choice. People often do not perform more than two to three steps of iterated deletion of dominated strategies. In simple one-shot games, such as the prisoner’s dilemma game, or in sequential games of full information, such as the centipede game, conformity with the predictions of the standard model is poor. People play mixed strategies, but often not a mixed strategy Nash equilibrium (MSE). Coordination failures are widespread, but many other factors, such as history dependence, aid in better coordination.

We consider models in behavioral game theory that better explain the evidence and their applications. The quantal response equilibrium (QRE) relaxes the assumption of best response, but the beliefs of players about others are accurate. Level-k models and the related cognitive hierarchy model (CH) are cognitively less challenging and assume that players play best responses, but their beliefs are not in equilibrium. We also consider models of cursed equilibrium that apply under imperfect information. Finally, we distinguish between private rationality and social rationality in the form of Kantian equilibrium and a correlated equilibrium.

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.

In this chapter, the authors study the applicability of quality-adjusted life years (QALYs) within the best-known extra-welfarist framework, Sen’s capability approach. They propose a procedure to value capability sets and provide a foundation for QALYs within Sen’s capability approach. They show that, under appropriate conditions, the ranking of capabilities can be represented locally by a QALY measure and that a willingness to pay for QALYs can be defined. The validity of QALYs as a general measure of health requires the same stringent conditions as in a welfarist framework. They consider the application of the proposed approach to the analysis of public health measures adopted in response to the COVID-19 pandemic.

This chapter focuses on stochastic frontier analysis studies of US hospitals, with an emphasis on 24 articles published since a review article by Rosko and Mutter in 2008. Stochastic frontier analysis (SFA) is the leading parametric technique used to analyze efficiency and productivity of hospitals. The chapter is organized around the five major ways in which hospital SFA studies have typically varied. The chapter also provides a summary of other study aspects such as sample size, geographic scope of study, whether efficiency was the dependent or independent variable, and important findings. While the older studies focused mainly on the correlates of hospital efficiency, the more recent studies had broader areas of inquiry including the association of efficiency with electronic medical record adoption, financial performance, patient satisfaction, patient care quality gaps, and wellness scores. The more recent studies also focused on consistency of estimates, policy analysis, and the use of SFA estimates of efficiency for benchmarking.

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.

Following the introduction of data envelopment analysis (DEA) in the operations research literature and its revival in the economics literature, for more than 40 years numerous studies have examined hospital efficiency, implementing the various advances in modeling efficiency and productivity. Many of these studies addressed the issues facing governments, hospitals, and patients. Three main concerns of these stakeholders are the cost of, quality of, and access to hospital care. This chapter focuses on research that used DEA to examine cost, quality, and access and the government policies that have been enacted to address these three concerns. Rather than a comprehensive literature review, the chapter discusses selected works that address relevant policy issues.

The hospital industry in many countries is characterized by right-skewed distributions of hospitals’ sizes and varied ownership types, raising numerous questions about the performance of hospitals of different sizes and ownership types. In an era of aging populations and increasing healthcare costs, evaluating and understanding the consumption of resources to produce healthcare outcomes is increasingly important for policy discussions. This chapter discusses recent developments in the statistical and econometric literature on DEA and FDH estimators that can be used to examine hospitals’ technical efficiency and productivity. Use of these new results and methods is illustrated by revisiting the Burgess and Wilson hospital studies of the 1990s to estimate and make inference about the technical efficiency of US hospitals, make inferences about returns to scale and other model features, and test for differences among US hospitals across ownership types and size groups in the context of a rigorous, statistical paradigm that was unavailable to researchers until recently.

Healthcare services are a major economic activity. Measurement of their value and volume in the national accounts is complicated because patients, providers, and insurers interact in multiple ways, often in a nonmarket setting, and because healthcare services undergo significant technical change. The monetary valuation of health services in the national accounts typically relies on a producer perspective, which may differ from the value that individuals attribute to health services. However, this consumer perspective cannot be ignored when it comes to quality-adjusting measured quantities of health services. Along with quality adjustment, the question of how to define and measure units of health services output needs answering. Methods vary among countries, some relying on volume measures of inputs while others gauge volume measures of treatment of diseases.

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.