Data Analytics & IT is focused on software tools necessary for data cleaning, retrieval, and management; data analyses and visualization; and analytics reporting for evaluation and implementation. Its Analytics Platform is a suite of tools, services, and datasets available to researchers across campus.

Leadership:

Megan Haymart, MD
Assistant Director

Megan Haymart is Professor of Medicine in the Division of Metabolism, Endocrinology, and Diabetes and the Department of Internal Medicine at the University of Michigan. She is also the Nancy Wigginton Endocrinology Research Professor in Thyroid Cancer. Dr. Haymart received her MD from Johns Hopkins University School of Medicine and completed her residency in internal medicine at Johns Hopkins Hospital. She completed a fellowship in endocrinology, diabetes, and metabolism at the University of Wisconsin. Dr. Haymart’s research interest lies in exploring how to improve cancer care delivery, with a focus on thyroid cancer, an understudied cancer that disproportionately affects women. Her research incorporates use of large claims data sources, cancer registries, and surveys. Her work has led to collaborative efforts with investigators across the University of Michigan campus.

Xiang Zhou is Professor of Biostatistics in the School of Public Health. He received his MS in Statistics and PhD in Neurobiology from Duke University in 2010, and completed postdoctoral training in Human Genetics at the University of Chicago. He was a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago before he joined the faculty at the University of Michigan in 2014. His research focuses on developing statistical methods and computational tools for genetic and genomic studies. By developing novel analytic methods, he seeks to extract important information from these data and to advance our understanding of the genetic basis of phenotypic variation for various human diseases and disease-related quantitative traits.

DA&IT Goals:

• Develop a secure, robust, flexible, and adaptable research Analytics Platform for Precision Health at U-M
• Facilitate a working group of faculty and researchers representing diverse perspectives within precision health
• Pursue corporate partnerships/external funding to strengthen U-M’s reputation as a leader in this space
• Design and create an accessible, secure computational environment
• Encourage and facilitate the development and sharing of reusable data analytics tools
• Develop IT infrastructure for the integration of clinical decision support tools, to enable real-time prospective validation capabilities
• Coordinate with Cohort Development on the integration, storage, and organization of multimodal data within the platform, to allow for broader campus-wide use.
• Coordinate with Health Implementation to enable fast dissemination of results, e.g., validated treatments and corresponding protocols
• Build a broader community of researchers and students with interests in data analytics for health, and establish interdisciplinary partnerships to tackle precision health challenges in new ways