Essays in Health and Behavioral Economics

Abstract

This dissertation consists of three chapters, each of which is an independent essay in the fields of health or behavioral economics. The first chapter explores the use of heuristics among highly-trained physicians diagnosing heart disease in the emergency department (ED), a common task with life-or-death consequences. Using data from a large private-payer claims database, I find compelling evidence of heuristic thinking in this setting: patients arriving in the ED just after their 40th birthday are roughly 10% more likely to be tested for and 20% more likely to be diagnosed with ischemic heart disease (IHD) than patients arriving just before this date, despite the fact that the incidence of heart disease increases smoothly with age. Moreover, I show that this shock to diagnostic intensity has meaningful implications for patient health, as it reduces the number of missed IHD diagnoses among patients arriving in the emergency department just after their 40th birthday, thereby preventing future heart attacks. I then develop a model that ties this behavior to an existing literature on representativeness heuristics, and discuss the implications of this class of heuristics for diagnostic decision-making. The second chapter examines the admitting decisions of ED physicians. Roughly half of all hospital admissions in the US flow through EDs, making emergency physicians important gatekeepers for expensive, high-intensity inpatient care. However, despite the high costs associated with hospital admissions, even physicians working in the same ED exhibit wide variation in their tendencies to admit patients to the hospital. This begs the question: are the additional admissions made by high-admitting physicians conferring benefits that are sufficient to outweigh their substantial costs? By exploiting quasi-random assignment of patients to physicians in a large Boston-area ED, I find that physicians with above-median admission rates are on average at least 20% more likely to admit a given patient than those with below-median admission rates. Although patients assigned to high-admitting physicians receive significantly greater treatment intensity, I find that this makes them no less likely to experience adverse health outcomes in the future. This suggests that these marginal admissions are of low value. The final chapter proposes a methodological contribution to the design of randomized control trials (RCTs), which are ubiquitous in the health sciences. Statistical power increases in the compliance rate of an experiment, so selecting participants with the highest likelihoods of compliance can provide the experiment with more power than if participants were chosen randomly. In this paper, I explore how data from prior experiments or quasi-experiments can allow researchers to systematically select the potential participants that are most likely to be compliers, and discuss the potential benefits and drawbacks of incorporating such an approach into RCT design. Using publicly available data from the Oregon Health Insurance Experiment, I empirically demonstrate the feasibility of these methods.