The Electronic Health Records (EHR) Research Pillar in the Division of Biostatistics is a dynamic initiative where faculty, PhD students, research scientists, and postdoctoral fellows actively engage in advancing EHR research. The group addresses a wide range of topics, including EHR sampling and modeling, their interaction with Social Determinants of Health (SDoHs), and their application to clinical management, public health inference, and comparative effectiveness research. A critical focus is on mitigating bias and disparities in these data. Current research efforts include statistical inference with non-random sampling and informative missingness in EHRs, dynamic risk prediction using longitudinal EHR predictors, and effectively incorporating SDoHs into study design and analysis. Additional topics include bias correction, algorithmic fairness, leveraging EHRs and SDoHs to optimize clinical trials, and utilizing national EHR resources for public policy evaluation and rare outcomes research.

The EHR working group fosters collaboration and knowledge exchange, meeting approximately twice per semester on the third Tuesday of a month from 11:00AM to 12:00PM. These meetings provide a forum to discuss ongoing projects and explore innovative methodologies in the rapidly evolving field of EHR research.

Lead Faculty

Rebecca Anthopolos. DrPH
Assistant Professor

Jiyuan Hu, PhD
Assistant Professor

Erinn M. Hade, PhD
Associate Professor

Faculty Members

Hayley Belli, PhD
Assistant Professor

Ivan L. Diaz, PhD
Associate Professor

Judith D. Goldberg
Professor

Matthew Lee, DrPH, MPH
Assistant Professor

Ting-Fang Lee
Research Assistant Professor

Sharon Meropol, MD, PhD
Research Assistant Professor

Yidan Shi, PhD
Assistant Professor

Hyungrok Do, PhD
Assistant Professor

Our Methodology Research

• Kim J, Anthopolos R, Zhong J. Bias correction models for electronic health records data in the presence of non-random sampling. Biometrics. 2024 Jan 29;80(1):ujae014. doi: 10.1093/biomtc/ujae014. PMID: 38488466; PMCID: PMC10941326.
• Do H, Nandi S, Putzel P, Smyth P, Zhong J. A joint fairness model with applications to risk predictions for underrepresented populations. Biometrics. 2023 Jun;79(2):826-840. doi: 10.1111/biom.13632. Epub 2022 Mar 27. PMID: 35142367; PMCID: PMC9363518.
• Do H, Putzel P, Martin A, Smyth P, Zhong J. Fair Generalized Linear Models with a Convex Penalty. Proc Mach Learn Res. 2022 Jul;162:5286-5308. PMID: 37016636; PMCID: PMC10069982

Our Collaborative Research

• Conderino S, Anthopolos R, Albrecht SS, Farley SM, Divers J, Titus AR, Thorpe LE. Addressing Information Biases within Electronic Health Record Data to Improve Examination of Epidemiologic Associations with Diabetes Prevalence among Young Adults: Cross-Sectional Study. JMIR Medical Informatics. Forthcoming.
• Chamberlain AM, Hade EM, Haller IV, Horne BD, Benziger CP, Lampert BC, Rasmusson KD, Boddicker K, Manemann SM, Roger VL. A large, multi-center survey assessing health, social support, literacy, and self-management resources in patients with heart failure. BMC Public Health. 2024 Apr 24;24(1):1141. doi: 10.1186/s12889-024-18533-7. PMID: 38658888; PMCID: PMC11040866.
• Hirsch, A.G., Conderino, S., Crume, T.L., Liese, A.D., Bellatorre, A., Bendik, S., Divers, J., Anthopolos, R., Dixon, B.E., Guo, Y. and Imperatore, G., 2024. Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network. BMJ open, 14(1), p.e073791.

Contact Us

For more information about the Electronic Health Records Research Pillar, please contact Rebecca Anthopolos, DrPH, at Rebecca.Anthopolos@NYULangone.org, Jiyuan Hu, PhD, at Jiyuan.Hu@NYULangone.org, or Erinn M. Hade, PhD, at Erinn.Hade@NYULangone.org.