While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data (RWD) has been vital in post-approval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of RWD is electronic health records (EHRs), which contain detailed information on patient care in both structured (e. g., diagnosis codes) and unstructured (e. g., clinical notes, images) form. Despite the granularity of the data available in EHRs, critical variables required to reliably assess the relationship between a treatment and clinical outcome can be challenging to extract. We provide an integrated data curation and modeling pipeline leveraging recent advances in natural language processing, computational phenotyping, modeling techniques with noisy data to address this fundamental challenge and accelerate the reliable use of EHRs for RWE, as well as the creation of digital twins. The proposed pipeline is highly automated for the task and includes guidance for deployment. Examples are also drawn from existing literature on EHR emulation of RCT and accompanied by our own studies with Mass General Brigham (MGB) EHR.
@article{arxiv.2211.16609,
title = {Harnessing electronic health records for real-world evidence},
author = {Jue Hou and Rachel Zhao and Jessica Gronsbell and Brett K. Beaulieu-Jones and Griffin Webber and Thomas Jemielita and Shuyan Wan and Chuan Hong and Yucong Lin and Tianrun Cai and Jun Wen and Vidul A. Panickan and Clara-Lea Bonzel and Kai-Li Liaw and Katherine P. Liao and Tianxi Cai},
journal= {arXiv preprint arXiv:2211.16609},
year = {2022}
}