With recent “omics” technology advances (genomics and otherwise), it is becoming more important to identify the most effective approaches to care for patients by bringing together population datasets with the best research evidence. In order to decipher the meaning of data collected from various sources (e.g., electronic health records, sensory technologies), we will need to develop linkages between drug and disease mechanisms and influences of variation in genes involved in those mechanisms. Two challenges to leveraging current approaches to use such diverse data sources are in: deciphering the meaning and value of data from multiple sources; and designing effective approaches to deliver new evidence. Examples ways to address these challenges through the iterative evaluation of methods that combine data from multiple sources, and by enabling the replicability and reproducibility of genomic variant interpretations will be described.
Speaker Biography
Dr. Casey Overby Taylor is Assistant Professor in the Divisions of General Internal Medicine and Health Sciences Informatics in the Department of Medicine at the Johns Hopkins University. She is also a Fellow in the Johns Hopkins Malone Center for Engineering in Healthcare. Her research interests span a number of areas at the intersection of public health genomics and translational bioinformatics, including applications that support translation of genomic research to clinical and population-based healthcare settings and delivering health information to the public. Dr. Taylor is co-Chair of the electronic health record implementation workgroup on the NHGRI-funded Electronic Medical Records and Genomics (eMERGE) Network, and an investigator on the NCATS Biomedical Data Translator grant. She also received a grant from AHRQ to explore the usefulness of a model to implement genomic clinical decision support that engages stakeholders and uses open source decision support platforms.