Biography: Jonathan Kolstad is a professor at the Haas School of Business, where he holds the Henry J. Kaiser Chair, and in the Economics Department at UC Berkeley. He is also a core faculty member in the Computational Precision Health Graduate Group at UC Berkeley and UCSF, the founding director of the Center for Health Care Marketplace Innovation and a Research Associate at the National Bureau of Economic Research. His research interests lie at the intersection of health economics, industrial organization and public economics. He is interested in finding new models and unique data that can account for the complexity of policy relevant markets, health care in particular. Much of his work applies tools from behavioral economics and machine learning and AI to better understand behavior and market outcomes and to design policy and technology interventions to improve welfare. Kolstad was awarded the ASHEcon Medal in 2018, given biennially to the economist age 40 or under who has made the most significant contributions to the field of health economics, the Arrow Award for the best paper in health economics in 2014 and the NIHCM Foundation Research Award in 2016 and 2018. Kolstad is also active as an entrepreneur and founder in health care and technology and an advisor to governments, corporations, and startups. He received his PhD from Harvard University and BA from Stanford University.

“Thinking versus Doing: Cognitive capacity, decision making and medical diagnosis”
Abstract: We study how situational fluctuations in cognitive capacity shape behavior in high-stakes, real-time decision-making. Drawing on recent advances in behavioral economics that revolve around inattention, cognition and complexity, we show that cognitive load influences how physicians in emergency departments allocate mental effort, attention, and make diagnostic and treatment decisions. We use quasi-random variation in patient-physician pairings, along with granular electronic medical record and audit-log data from many clinical interactions, to show that, under higher cognitive load, physicians substitute mental deliberation with more numerous but less precise diagnostic actions. Specifically, we document that higher load (i) increases the total number of orders of diagnostic tests, (ii) reduces the use of more precise and less common tests (iii) increases the use of common tests and (iv) increases uncertainty in diagnostic beliefs. We show that cognitive load impacts downstream inpatient admission from the emergency department: a physician in the highest cognitive load decile increases admissions by 28% relative to the same physician in the lowest cognitive load decile, for the exact same kind of patient. We also explore implications for policy, including how patient-physician matching might be improved by accounting for cognitive load profiles. These results offer novel field-based evidence on the dynamics of attention and belief formation, and shed light on how cognitive constraints shape diagnostic behavior in complex, real-world environments.
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