Category: Other - Immunology
Results from flow cytometry assays on real-world patients are routinely stored in Electronic Health Records (EHRs) but using these data for research is prohibitive due to data privacy concerns. Furthermore, the limited availability of healthy controls in EHRs makes interpretatibility difficult. To leverage the wealth of clinical flow cytometry within EHR systems, we have developed a tool, the Clinical Immunomes Dashboard for EHRs (CIDEHR; pronounced “CIDER”), for mining EHR flow cytometry results and comparing to the 10k Immunomes cohort (http://10kimmunomes.org), a collection of immune measurements from healthy individuals from open-access clinical studies. CIDEHR allows users to build and visualize EHR cohorts of interest with the ability to filter patients by diagnosis and medication records while comparing to the healthy 10k Immunomes. The tool can be employed within a secure environment using OMOP or Epic Caboodle, commonly used EHR formats, and users can utilize the full functionality of the application without the ability to view Protected Health Information (PHI). For example, using the UCSF EHR, CIDEHR has the ability to visualize flow cytometry and other clinical information from 5,330 patients, which will soon grow in population size with the inclusion of multiple University of California institutions. This simple tool enables researchers to study real-world populations in the EHR without the need to relinquish PHI, which could serve as a model for more types of data using more interconnected EHRs in the future.
Thomas Peterson– Postdoctoral Scholar, UCSF Bakar Computational Health Sciences Institute
Benjamin Glicksberg– Postdoctoral Scholar, UCSF Bakar Computational Health Sciences Institute
Zicheng Hu– Postdoctoral Scholar, UCSF Bakar Computational Health Sciences Institute
Sanchita Bhattacharya– Bioinformatics Project Leader, UCSF Bakar Computational Health Sciences Institute
Atul Butte– Director, UCSF Bakar Computational Health Sciences Institute