Adrienne Sabety, PhD, is a Wilson Family LEO Assistant Professor in the Department of Economics at the University of Notre Dame. She is also a research faculty member at the Wilson Sheehan Lab for Economic Opportunities at Notre Dame. Her research focuses on access to care and treatment for disadvantaged populations, like undocumented immigrants. She also studies major changes in health care markets, such as the design of insurance marketplaces and adverse climate shocks. She received an AS in Mathematics from Cuesta Community College, a BA in Economics from UC Berkeley, and a PhD in Health Policy from Harvard in 2020.

Stephen Kissler is an infectious disease epidemiologist at the Harvard T.H. Chan School of Public Health. He earned his PhD in Applied Mathematics at the University of Cambridge as a Gates Scholar, where he studied the transmission of the 2009 H1N1 influenza pandemic. At Harvard, Stephen has focused on identifying drivers of antibiotic resistance and, more recently, on SARS-CoV-2 response. In addition to his research, he has consulted with Partners in Health and provided comments for numerous media outlets during the COVID-19 pandemic. 

Joshua Salomon, PhD, is a Professor of Health Policy and a Senior Fellow at the Freeman Spogli Institute for International Studies. His research focuses on public health policy and priority-setting, including modeling patterns and trends in major causes of global mortality and disease burden; evaluation of health interventions and policies; and measurement and valuation of health outcomes. He is director of the Prevention Policy Modeling Lab, a multi-institution research consortium that conducts health and economic modeling relating to infectious disease. During the COVID-19 pandemic, Dr. Salomon has worked extensively with policymakers on data synthesis, modeling and decision analysis to inform the public health response.

David Chan, MD, PhD, is an Associate Professor of Medicine and an investigator at the Department of Veterans Affairs, and a Faculty Research Fellow at the National Bureau of Economic Research. Drawing on labor and organizational economics, he is interested in studying how information is used in health care, how this affects productivity, and implications for design. He is the recipient of the 2014 NIH Director’s High-Risk, High-Reward Early Independence Award to study the optimal balance of information in health information technology for patient care.

Sherri Rose, PhD  is an Associate Professor of Health Policy at the Stanford School of Medicine and Co-Director of the Health Policy Data Science Lab. Her research is centered on developing and integrating innovative statistical machine learning approaches to improve human health and health equity. Within health policy, Dr. Rose works on risk adjustment, ethical algorithms in health care, comparative effectiveness research, and health program evaluation. She has published interdisciplinary projects across varied outlets, including Biometrics, Journal of the American Statistical Association, Journal of Health Economics, Health Affairs, and New England Journal of Medicine. In 2011, Dr. Rose coauthored the first book on machine learning for causal inference, with a sequel text released in 2018. She has been Co-Editor-in-Chief of the journal Biostatistics since 2019.

Dr. Rose has been honored with an NIH Director's New Innovator Award, the ISPOR Bernie J. O'Brien New Investigator Award, and multiple mid-career awards, including the Gertrude M. Cox Award and the Mortimer Spiegelman Award, the nation’s highest honor in biostatistics, given to a statistician younger than 40 who has made the most significant contributions to public health statistics. She was named a Fellow of the American Statistical Association in 2020 and received the 2021 Mortimer Spiegelman Award, which recognizes the statistician under age 40 who has made the most significant contributions to public health statistics. Her research has been featured in The New York Times, USA Today, and The Boston Globe. 

Title: New and Ongoing Projects at the Interface of Machine Learning for Health Policy

 

Title: Customer Discrimination and Quality Signals: A Field Experiment with Healthcare Shoppers

Abstract: This paper provides evidence that customer discrimination in the market for doctors can be largely accounted for by statistical discrimination. I evaluate customer preferences in the field with an online platform where cash-paying consumers can shop and book a provider for medical procedures based on an experimental paradigm called validated incentivized conjoint analysis (VIC). Customers evaluate doctor options they know to be hypothetical to be matched with a customized menu of real doctors, preserving incentives. Racial discrimination reduces patient willingness-to-pay for black and Asian providers by 12.7% and 8.7% of the average colonoscopy price respectively; customers are willing to travel 100–250 miles to see a white doctor instead of a black doctor, and somewhere between 50–100 to 100–250 miles to see a white doctor instead of an Asian doctor. Further, providing signals of provider quality reduces this willingness-to-pay racial gap by about 90%, which suggests that statistical discrimination is an important cause of the gap. Actual booking behavior allows cross-validation of incentive compatibility of stated preference elicitation via VIC. 

Alex Chan, MPH

Alex Chan is a PhD candidate in Health Economics, and a Gerhard Casper Stanford Graduate Fellow. He has research interests in health economics, experimental economics, market design, and labor economics. His projects look at the causes and consequences of discrimination and diversity in medicine, U.S. Health Policy (especially organ transplantation), and market design in health policy and medicine. He holds an MPH from Harvard University. Before Stanford, he developed extensive experience in the healthcare industry starting as a McKinsey consultant, and most recently as Senior Vice President of Market Strategy with Optum/UnitedHealth before joining academia.

Personal Website: https://www.alexchan.net 

Timothy J. Layton, PhD

Associate Professor of Health Care Policy, Department of Health Care Policy, Harvard Medical School

His research focuses on the economics of health insurance markets with particular emphasis on understanding insurer behavior in those markets and designing optimal health plan payment systems. 

Dr. Layton and his collaborators are using economic models of health insurer behavior to design payment systems that combat inefficiencies caused by adverse selection. In one project, he and his coauthors are deriving new methods for designing health plan payment systems that set payments to insurers in a way that discourages insurers from inefficiently rationing care used by sick individuals with multiple chronic conditions. This work focuses on designing payment systems for the state and federal Health Insurance Marketplaces, as well as the Dutch health insurance market and the Medicare Advantage program.

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Achyuta Adhvaryu, PhD. 

Alexander M. Nick Professor
Associate Professor of Business Economics and Public Policy
University of Michigan, Ross Schoo lof Business 

 

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Amanda Starc, Ph.D.

Associate Professor of Strategy at the Kellogg School of Management
Faculty Research Fellow at the National Bureau of Economic Research (NBER)

Professor Amanda Starc received her BA in Economics from Case Western Reserve University, and her PhD in Business Economics from Harvard University. Dr. Starc's research interests include industrial organization and health economics. Her research examines the Medicare Advantage, Medicare Part D, and Medicare Supplement ("Medigap") markets, as well as consumer behavior in insurance exchanges. Recent work measures the effectiveness of direct-to-consumer advertising of pharmaceuticals. Her work links models of consumer choice and supply side incentives, and uses a range of econometric techniques to analyze data.

This will be an in-person event: Encina Commons, Conference Rom 119, with a boxed lunch served.