Using Electronic Health Records for Scientific Research: Promises and Perils

Professor Bhramar Mukherjee

Bhramar MukherjeeLink opens in a new window is the John D. Kalbfleisch Collegiate Professor and chair of the Department of Biostatistics at the University of Michigan School of Public Health. She is also a professor in the Department of Epidemiology and a professor of Global Public Health at the School of Public Health.

Mukherjee's research focuses on the development and application of statistical methods in epidemiology, environmental health, cancer research and disease risk assessment. She has authored more than 340 publications in statistics, biostatistics, epidemiology and medical journals and has led several impactful extramural grants as a principal investigator from both the NSF and the NIH.

Dedicated to diversifying the statistical and data science workforce, Mukherjee has been leading a flagship undergraduate summer program in Big Data since 2015. This program has trained nearly 300 undergraduates, more than 60% of whom go on to pursue graduate school in a quantitative field. She is a fellow of the American Statistical Association and the American Association for the Advancement of Science. She is recipient of many awards, including the Janet Norwood Award and the Sarah Goddard Power award in 2021. In 2022, she was elected to the National Academy of Medicine, one of the highest honors for researchers in health and medicine.

Using Electronic Health Records for Scientific Research: Promises and Perils

Electronic Health Records (EHR) linked with other auxiliary data sources hold tremendous potential for conducting real time actionable research. However, one has to answer two fundamental questions before conducting inference: "Who is in my study?" and "What is the target population of Inference?". Without accounting for selection bias, one can quickly produce rapid but inaccurate conclusions. In this talk, I will discuss a statistical framework for jointly considering selection bias and phenotype misclassification in analyzing EHR data. Examples will include genome and phenome-wide association studies of Cancer and COVID-19 outcomes using data from the Michigan Genomics initiative and the UK Biobank. This is joint work with Lars Fritsche, Lauren Beesley and Maxwell Salvatore at the University of Michigan School of Public Health.

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