Omri Gottesman, MBBS
- ADJUNCT ASSISTANT PROFESSOR | Medicine, General Internal Medicine
Dr. Gottesman is a UK-trained physician-scientist focused on the translation and implementation of genomic and data-driven medicine into clinical practice.
MD, University College London
Dr. Gottesman is currently leading the CLIPMERGE PGx program (CLinical Implementation of Personalized Medicine through Electronic health Records and GEnomics) at Mount Sinai. The CLIPMERGE program aims to implement a novel clinical decision support (CDS) engine for delivering guidelines for using genetic variants of clinical significance in a way that integrates with physician work processes. CLIPMERGE is an advanced information management system that is external to, but communicates with the Electronic Health Record and takes advantage of decision support capabilities executable at the point-of-care. The pilot study, CLIPMERGE PGx, is focused on pharmacogenomics and will consent 1500 Biobank participants undergoing routine care at Mount Sinai Medical Center and genotype them for known, common pharmacogenomic variation. These results will be made available to the patient's treating physician at the time they are prescribing certain medications. Researchers will track whether or not this information is used in routine health care to make prescribing decisions and the reasons behind whether or not the decision support was used.
Dr. Gottesman’s work focuses on developing and evaluating tools and best-practices that will facilitate the translation and implementation of genomic and data-driven medicine into clinical practice today. Much of this involves the Institute for Personalized Medicine (IPM) BioBank – a clinical cohort of more than 22,000 Mount Sinai Hospital patients that have donated their DNA to an Electronic Medical Record-linked DNA biorepository and have consented to research on a de-identified image of their medical record and to re-contacting for clinical trials. His work at the IPM has included clinical data representation and electronic phenotyping – creating case definition algorithms that leverage information from the electronic medical record to automatically assign case and control status for use in Genome Wide Association Studies (GWAS), replication studies and implementation trials. He is a co-investigator for a NHGRI U01 consortium - eMERGE II (Electronic MEdical Records and GEnomics). His focus is predominantly on complex trait genomics and pharmacogenomics as they relate to the general medical patient and in clinical data capture and representation.
- Developing innovative tools to facilitate personalized medicine
- Electronic phenotyping - clinical data capture and representation
- Genomic and data-driven medicine - translation of genomics and clinical data into enhanced clinical prediction and clinical care through electronic health records and clinical decision support
- Complex trait genomics and associated pharmacogenomics
- Genomic medicine in heterogeneous populations
- Clinical care implementation and evaluation of personalized medicine projects