Grand rounds: The ENACT Network

This presentation, led by Griffin Weber of Beth Israel Deaconess Medical Center and Harvard Medical School, discusses how healthcare system processes, like clinic schedules and billing practices, lead to apparent “data quality” issues when using electronic health records (EHRs) for research. Several approaches, including machine learning-based “computed phenotypes,” are described. These algorithms, integrated into the i2b2 software platform, are used at several ENACT network sites.

The ENACT Grand Rounds, a monthly seminar series hosted by the ENACT Network, fosters learning on real-world data (RWD) and real-world evidence (RWE) generation, promoting an open science and data sharing culture. The series accelerates scientific discovery and improves translational research efficiency, quality, and impact by exploring Electronic Health Records (EHR) in solving problems like secondary data analysis, surveillance, learning healthcare systems, and policy.