Job Market Paper
X-raying Experts: Decomposing Mistakes in Radiology
Human experts often err. How many of these errors are preventable mistakes, and what drives them? I study these questions in the high-stakes field setting of radiology. Using anonymized health records from a large hospital, I compare radiologists' findings of cardiac dysfunction on chest x-rays to algorithmic predictions, adjudicating between the two with exogenously administered blood tests. I find that at least 46 percent of radiologists systematically mis-rank patients for signs of cardiac dysfunction. A decomposition shows that errors reflect individual radiologists falling short of best clinical practice (a "human frontier"), and a further gap between best practice and algorithmic predictions (a "machine frontier"). Raising radiologists to the human frontier could increase their true positive rates by 6% or decrease false positives by 20%; raising them to the machine frontier would further increase true positives by 4% or decrease false positives by 14%. Examining the incidence of error, I find evidence for behavioral inattention: radiologists react appropriately to salient details such as a patient's age and symptoms, but under-react to complex signals captured by algorithmic predictions.
Publications
When Guidance Changes: Government Stances and Public Beliefs (with Charlie Rafkin and Pierre-Luc Vautrey)
Journal of Public Economics, April 2021
Governments often make early recommendations about issues that remain uncertain. Do governments’ early positions affect how much people believe the latest recommendations? We investigate this question using an incentivized online experiment with 1900 US respondents in early April 2020. We present all participants with the latest CDC projection about coronavirus death counts. We randomize exposure to information that highlights how President Trump previously downplayed the coronavirus threat. When the President’s inconsistency is salient, participants are less likely to revise their prior beliefs about death counts from the projection. They also report lower trust in the government. These results align with a simple model of signal extraction from government communication, and have implications for the design of changing guidelines in other settings.
Working Papers
Managing Emotions: The Effects of Online Mindfulness Meditation on Mental Health and Economic Behavior (with Pierre-Luc Vautrey)
Mindfulness meditation has gained popularity, fueled by accessible smartphone apps and rising concerns about mental health. While such apps are claimed to affect mental well-being, produc- tivity, and decision making, existing evidence is inconlcusive due to limited sample sizes and high attrition. We address these concerns by conducting a large-scale, low-attrition experiment with 2,384 US adults, randomizing access and usage incentives for a popular mindfulness app. App access improves an index of anxiety, depression, and stress by 0.38 standard deviations (SDs) at two weeks and 0.46 SDs at four weeks, with persistent effects three months later. It also improves earnings on a focused proofreading task by 2 percent. However, we find near-zero effects on a standard cognitive test (a Stroop task), and on decisions over risk and information acquisition where past economics research has indicated that emotions affect choice. This study provides evidence that digital mindfulness improves mental health and can raise productivity, but suggests that these effects do not stem from traditional measures of cognitive skills nor do they accompany more primitive changes in the information and risk preferences we measure.