This chapter covers the medical malpractice system: how it works, what its goals are, and how it influences provider behavior. The chapter begins by defining key terminology in tort law and explaining the process by which a medical malpractice case is brought and resolved, as well as the goals that this system is trying to achieve. Then discussion turns to how this system creates incentive for actions that run counter to its goals and the problems that are likely to arise, along with some empirical evidence of the existence of said problems.

This chapter introduces the basics of the economic approach to understanding decision-making. This is done using examples drawn from consumer decision-making in the context of healthcare. Topics include how to think about preferences, different types of costs, optimization, and the importance of perceptions. The end of chapter supplement discusses how to use price indexes.

Chapter 14 provides a brief overview of major health insurance provided by the government in the United States. The majority of the chapter goes through the major programmatic details of the Medicare and Medicaid Programs: who is covered, what services are covered and how generously, and how providers are paid by the programs. The discussion of Medicaid also explains the nature of the federal-state partnership and explores ways in which states have and use their freedom to expand upon the program. The remainder of the chapter briefly describes other public insurance (or insurance-related) programs: the VA and CHAMPVA, TRICARE, CHIP, COBRA, and the IHS.

This chapter introduces Spark, a data processing engine that mitigates some of the limitations of Hadoop MapReduce to perform data analytics efficiently. We begin with the motivation of Spark, introducing Spark RDDs as an in-memory distributed data structure that allows for faster processing while maintaining the attractive properties of Hadoop, such as fault tolerance. We then cover, hands-on, how to create and operate with RDDs, distinguishing between transformations and actions. Furthermore, we discuss how to work with key–value RDDs (which is more like MapReduce), how to use caching to perform iterative queries/operations, and how RDD lineage works to ensure fault tolerance. We provide a great range of examples with transformations such as map vs. flatMap and groupByKey vs. reduceByKey, discussing their behavior, adequacy (depending on what we want to achieve), and their performance. More advanced concepts, such as shared variables (broadcast and accumulators) or work by partitions are presented towards the end. Finally, we talk about the anatomy of a Spark application, as well as the different types of dependencies (narrow vs. wide) and the limitations on optimizing their processing.

Chapter 5 moves from theory into evidence and discusses how empirical economists think about causality. First, the chapter covers common issues that make it difficult to have confidence in causal claims based on associational evidence alone. Then, experimental evidence is discussed: how to run an experiment, common pitfalls that can undermine confidence in experimental evidence, and what can be done to avoid them. Next, major experimental studies on the impact of health insurance are described. Finally, the chapter discusses the concept of quasi-experimental evidence and how it fits into economics. The end of chapter supplement discusses ethics in research with human subjects and the role of institutional review boards.

Chapter 3 develops the fundamentals of demand analysis through the lens of demand for medical care. The chapter discusses how to read and use demand curves and why demand for health is not commonly used (there is no direct market for health). Then various demand elasticities are covered: price elasticity of demand, income elasticity of demand, and cross-price elasticity of demand. Each elasticity is developed along with examples specific to the market for medical care and health decision-making. The chapter also develops important tools for using demand curves: how to think through demand shifters, Engel curves, how to calculate consumer surplus, and how to aggregate from individual to market demand. The end of chapter supplement walks through how to calculate elasticities.

This chapter focuses in on how individuals invest in or harm their health. This is done using the concept of the health production function. Various inputs to the function such as medical care, illness and injury, lifestyle choices, age, genetics, and environmental factors are discussed along with their interactions with each other. Key health economics concepts such as the flat of the curve, the tradeoff between health and utility, and the role of technological innovation in medical care effectiveness are discussed, as well as the key economic concept of the margin. The end of chapter supplement introduces the concept of standardized units for measuring effectiveness of care.

In this chapter we cover clustering and regression, looking at two traditional machine learning methods: k-means and linear regression. We briefly discuss how to implement these methods in a non-distributed manner first, to then carefully analyze the bottlenecks of these methods when manipulating big data. This enables us to design global-based solutions based on the DataFrame API of Spark. The key focus is on the principles for designing solutions effectively. Nevertheless, some of the challenges in this chapter are to investigate tools from Spark to speed up the processing even further. k-means is an example of an iterative algorithm, and how to exploit caching in Spark, and we analyze its implementation with both RDD and DataFrame APIs. For linear regression, we first implement the closed form, which involves numerous matrix multiplications and outer products, to simplify the processing in big data. Then, we look at gradient descent. These examples give us the opportunity to expand on the principles of designing a global solution, and also allow us to show how knowing the underlying platform, Spark in this case, well is essential to really maximize the performance.

Spark SQL is a module in Spark for structured data processing, which improves upon RDDs. The chapter explains how imposing structure on the data helps Spark perform further optimizations. We talk about the transition from RDDs to DataFrames and Datasets, including a brief description of the Catalyst and Tungsten projects. In Python, we don’t have Datasets, and we focus on DataFrames. With a learn-by-example approach, we see how to create DataFrames, inferring the schema automatically or manually, and operate with them. We show how these operations usually feel more natural for SQL developers, but we can interact with this API following an object-oriented programming style or SQL. Like we did with RDDs, we showcase various examples to demonstrate the functioning of different operations with DataFrames. Starting from standard transformations such as select or filter, we move to more peculiar operations like Column transformations and how to perform efficient aggregations using Spark functions. As advanced content, we include implementing user-defined functions for DataFrames, as well as an introduction to pandas-on-Spark, a powerful API for those programmers more used to pandas.

This chapter introduces the concept of insurance as a product and explores why people want to purchase insurance in general (and health insurance in particular). The main discussion centers around explaining that health insurance (and all insurance) is primarily financial protection: health insurance does not protect your health but instead protects your wealth from health-related risk. The chapter then moves on to discuss the operations of an insurance company: how premiums are set, the difference between correlated and uncorrelated risk, group insurance, and experience rating. The chapter ends by discussion moral hazard in the context of an individual with insurance coverage. The end of chapter supplement provides a mathematical example of why someone who is risk averse would want to purchase insurance.