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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Stacie B. Dusetzina, Ph.D

Associate Professor, Health Policy
Ingram Associate Professor of Cancer Research, Vanderbilt University Medical Center

Dr. Dusetzina is an associate professor in the Department of Health Policy and an Ingram associate professor of cancer research at Vanderbilt. She is a health services researcher whose work focuses on measuring and evaluating population-level use and costs of medications in the United States. Dr. Dusetzina’s work has contributed to the evidence base for the role of drug costs on patient access to care and policy changes that might improve patient access to high-priced drugs.

She has been recognized for her work at a national level, including being an invited participant for two working group meetings on “Patient Access to Affordable Cancer Drugs,” hosted by the President’s Cancer Panel, and being selected to co-author a National Academies of Sciences, Engineering and Medicine report on the same topic. Dr. Dusetzina’s research has also been broadly covered by The New York Times, NPR, Reuters, The Washington Post, STAT News, ABC News and The Wall Street Journal. 

In addition to her work on drug pricing, Dr. Dusetzina is a population health scientist and pharmacoepidemologist specializing in large data informatics. She has authored or co-authored more than 163 peer reviewed applied studies using Medicaid, Medicare, and commercial insurance claims data, and contributed several methods papers to the field. 

Seminar Title: Improving Access to Prescription Drugs through Policy Change