Stanford Health Policy’s newest faculty member: Sherri Rose
Sherri Rose comes to us from Harvard Medical School, where she co-founded the Health Policy Data Science lab.
Sherri Rose, PhD, an expert on statistical machine learning for health services research, joins Stanford Health Policy in August as our newest associate professor of medicine. She comes to Stanford from Harvard Medical School, where she was an associate professor in the Department of Health Care Policy. Dr. Rose’s career accolades span fields, including the ISPOR Bernie J. O’Brien New Investigator Award for her scholarly work in health economics and outcomes and a Mid-Career Award from the Penn-Rutgers Center for Causal Inference for her achievements in the development and application of innovative causal inference methods. This summer she will be inducted as a Fellow of the American Statistical Association.
“Sherri is an extraordinary scholar and teacher,” said Douglas K. Owens, director of Stanford Health Policy. “Her work spans policy, biostatistics, and machine learning, among other areas. We very much look forward to working with her to develop new methods in data science and to address pressing health policy questions.”
Rose talks about her research and expectations for this next chapter in her career.
Q. Why Stanford and in particular Stanford Health Policy?
Stanford is a unique intellectual environment with incredible strengths across every discipline in my research: health policy, statistics, economics, machine learning and AI. The interdisciplinary focus at Stanford Health Policy is a big draw, with fantastic scholars asking and answering crucial policy questions. I’m also looking forward to expanding my research in health inequities, among other areas of emphasis at SHP. I’ll be joining the tenured faculty at Stanford on August 1st, although given the current circumstances of the pandemic, won’t be on campus yet. Fortunately, my research is conducive to remote work, and I’ll be getting to know SHP faculty, students, and fellows through video, voice calls, and email until it’s safe to gather on campus again.
Q. You are the co-director of the Health Policy Data Science Lab. Tell us about the Lab and explain your work there in statistical machine learning.
I co-founded the Health Policy Data Science Lab five years ago with my colleague Laura Hatfield. The Lab is an inclusive, student-centered space where we develop and apply statistical methodology for health policy. It will now be joint across Harvard and Stanford and I’m excited to bring Stanford trainees into the community we’ve created. The methods I develop are largely statistical machine learning approaches, which 'smooth' over data more flexibly than standard techniques. This can allow us to discover information we wouldn’t otherwise surface or estimate the impact of a policy with less bias.
Q. While at Harvard, you received a prestigious $2.5 million NIH Director’s New Innovator Award for integrating innovative statistical approaches to advance human health. Will you bring that award with you?
Yes, I’ll be bringing my grant with me to Stanford. The broad topic of the grant is in generalizability—how well do the findings from a particular study extend to the larger population we care about? I’ve been working on a series of projects under this broad umbrella, including new methods for generalizing from both observational and randomized data as well as novel approaches for algorithmic fairness in the health care system.
Q. How does the field of health economics inspire you?
I love working on the computational health economics tools I develop because they may have a direct impact on individual lives in the health care system. For example, my work in plan payment risk adjustment is particularly rewarding because financing changes can lead to major gains in access to care and improved health outcomes. Tens of millions of people in the U.S. are enrolled in an insurance product that risk adjusts payments, and the standard regression techniques currently in use have created various inequities. This makes risk adjustment one of the most consequential applications of regression for social policy. I’ve directed a series of projects proposing a computational re-evaluation of plan payment risk adjustment, including fulfilling collaborations with economists and health policy students.
Q. How has your personal background informed your research?
I grew up in a low-income household and my life experiences absolutely inform my perspectives in research. How I ask questions, who is centered in the research, and my understanding of the need for diverse teams comes from this, as well as years of listening to a broad set of voices.
Q. Tell us something about yourself that most people don’t know.
I’ve run six marathons, several of them with my close friend Soozie.
Q. Who else making this cross-country move with you?
I’m returning to the Bay Area with my husband Burke, who is a systems administrator at UC Berkeley, and our pets. Our 15-year-old cat Tobias has been with us since he was a kitten and I was a graduate student in biostatistics at UC Berkeley.