معرفی
Sharon-Lise Normand is the S. James Adelstein Professor of Health Care Policy (Biostatistics) at Harvard Medical School and Professor of Biostatistics at Harvard T.H. Chan School of Public Health. Her research focuses on statistical methodologies for health services and outcomes research, particularly Bayesian approaches applied to medical device evaluation, causal inference, provider profiling, and evidence synthesis. She leads the Medical Device Epidemiology Network’s Methodology Center and previously directed Mass-DAC, a cardiac surgery quality monitoring initiative. Normand’s work spans cardiovascular disease, severe mental illness, and medical technology diffusion. She has held leadership roles in professional organizations like the American Statistical Association and received multiple awards for her contributions to biostatistics and healthcare research.
Dr. Normand holds a PhD from the University of Toronto and completed a postdoctoral fellowship at Harvard Medical School. Her honors include Fellowships in the American Statistical Association, American College of Cardiology, and American Association for the Advancement of Science. She has advised federal agencies such as the FDA and CMS and contributed to policy developments in healthcare quality and equity.
Her research team includes experts in biostatistics and data analysis, focusing on topics like telemedicine adoption disparities, racial/ethnic healthcare quality gaps, and the evaluation of medical device safety. Recent work examines pandemic impacts on healthcare access and methodological innovations in causal inference and composite outcome analysis.
Normand’s scientific awards reflect her impactful contributions, including the 2017 American Heart Association Lifetime Achievement Award and the 2018 Mosteller Statistician of the Year. She has authored over 200 peer-reviewed publications, with recent studies addressing opioid medication availability, telemedicine trends, and disparities in mental healthcare access under value-based payment models.
