Using Electronic Health Records to Support Patient Care and Clinical Research

Abstract

Electronic health records (EHR) contains rich clinical phenotype information. In this talk, I will present methods and early results from two projects to demonstrate the potential of using EHR data to facilitate precision medicine and optimize clinical research towards a learning health system. In project one, we developed a phenotype-driven diagnostic decision support system, where Human Phenotype Ontology (HPO) concepts were extracted from EHR narratives and used to prioritize disease genes based on the HPO-coded phenotypic manifestations. We tested this approach on 28 pediatric patients with confirmed diagnoses of monogenic diseases, and found that the causal genes were ranked among the top 100 genes out of > 25000 genes for 16/28 cases (P<2.2x10-16), demonstrating the promise of leveraging EHR data to automate phenotype-driven analysis of clinical exomes or genomes and implement genomic medicine on scale. In project two, we developed a metric called GIST, which stands for The Generalizability Index of Study Traits, to assess the population representativeness of clinical trials by using EHR data to profile the target populations for clinical trials and by comparing the study populations to the target populations. GIST enables us to improve the transparency of population representativeness of clinical studies and to help clinical researchers to make informed decisions to optimize patient selection.