Equity and bias in electronic health records data
Andrew D. Boyd, Rosa M. González‐Guarda, Katharine Lawrence, Crystal L. Patil, Miriam O. Ezenwa, Emily C. O’Brien, Hyung Paek, Jordan M. Braciszewski, Oluwaseun Adeyemi, Allison M. Cuthel, Juanita E. Darby, Christina K. Zigler, P. Michael Ho, Keturah R. Faurot, Karen L. Staman, Jonathan W. Leigh, Dana L. Dailey, Andrea Cheville, Guilherme Del Fiol, Mitchell R. Knisely, Keith Marsolo, Rachel Richesson, Judith M. Schlaeger
Abstract
Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity.