To provide greater clinical insight for the fight against COVID-19, a consortium of research scientists that included faculty from the Perelman School of Medicine at the University of Pennsylvania pooled their efforts to create a common data model and a shared analytics framework that will aggregate information from disparate electronic health records (EHR) internationally. With the creation of this model—the Consortium for Clinical Characterization of COVID-19 by EHR (4CE)—clinical teams and researchers will now have a powerful tool available to them to quickly discover trends and provide answers to questions about the virus. A paper on the effort was published in August in NPJ Digital Medicine.
Like the other sites in the study, clinical data from Penn Medicine was analyzed and provided for the effort. And for future studies with 4CE, PennAI, a free self-service machine learning tool developed at the Institute for Biomedical Informatics, will be available to each member site to power the project.
“We are excited to use our PennAI software for this project,” says paper co-author Jason Moore, the director of the Institute for Biomedical Informatics and a professor of informatics. “It can be installed locally at each site and used to generate machine learning models for predicting COVID-19 outcomes such as death or disease severity. This is a critical need that we will contribute to the project.”
The consortium consists of 96 hospitals from around the world and so far, has gathered data on more than 27,000 COVID-19 cases with 187,000 laboratory tests. Previously, because of differences in electronic health records, all of this data would not have been able to “talk” to each other in a way necessary for analysis. But with so many sites putting their data into a common data model and making it available to be processed and analyzed, consortium scientists were able to detect trends and patterns of this new virus that were previously invisible.
This story is by Frank Otto. Read more at Penn Medicine News.