May 8th, 2014, Atlanta, Ga. – The Charles Bronfman Institute for Personalized Medicine (IPM) at the Icahn School of Medicine at Mount Sinai has deployed Clinithink’s CLiX technology to support the practice and development of personalized medicine. CLiX forms part of a solution that enables patients at Mount Sinai to receive more targeted, personalized care in real-time based on their own DNA.
IPM has worked with Clinithink to integrate CLiX into the technology platform that underpins the Clinical Implementation of Personalized Medicine through Electronic Health Records and Genomics (CLIPMERGE) Program at Mount Sinai. CLiX provides Clinical Natural Language Processing (CNLP) to support real-time “electronic phenotyping” being pioneered at IPM by identifying potentially relevant clinical attributes from rich narrative data stored within Mount Sinai’s Electronic Medical Record (EMR) and passing them to the CLIPMERGE decision support engine.
The resulting solution facilitates the provision of guidance to physicians in real-time about prescribing strategies that take the patient’s genome into account. This advance in patient care stems from the ground-breaking BioMe Biobank program at Mount Sinai, in which 30,000 patients have enrolled to make their genetic data available to support research in this important new medical field. Combining IPM’s CLIPMERGE platform with Clinithink’s CLiX technology can benefit patients through the correlation of genomic data held in BioMe with phenotypic data, contained only in narrative text from the EMR, enabling the delivery of personalized medicine.
Assistant Professor of Medicine, Dr. Omri Gottesman, said “Our first use of CLiX has been to enable physicians prescribing Simvastatin to receive decision support when faced with patients who have certain variants in the SLCO1B1 gene. These patients could be at risk of adverse events if they receive higher doses of simvastatin. We provide this advice when our system detects these situations, preventing potentially adverse outcomes for the patient. However, we could not do this without the integral use of CLiX to analyze the prescribing data we receive from the EMR.”
Steve Ellis, Senior Director, Informatics and IT at the Charles Bronfman Institute for Personalized Medicine at Mount Sinai, said “We evaluated a number of Clinical Natural Language Processing technologies against our requirements for speed and accuracy, and we chose Clinithink’s CLiX technology. We are planning a more extensive use of CLiX as the deployment of CLIPMERGE continues.”
Dr. Chris Tackaberry, Clinithink CEO, said “In an increasingly competitive market we are delighted that the Institute of Personalized Medicine at Mount Sinai has selected Clinithink as their CNLP. We believe that our CLiX technology adds significant value to personalized medicine by enabling phenotype extraction. Our collaboration with IPM resulting in prescribing decision support is an exciting step in the growth of the company.”
About The Charles Bronfman Institute for Personalized Medicine
The Institute is a world-leading, innovative centre for data-driven and gene-based individualization of healthcare. Its mission is to personalize disease risk assessment and improve responsiveness to treatments for patients through innovative translational genomics and informatics research. Additionally, through its research, its goal is to pioneer advancements in healthcare for patients and providers through the delivery of personalized, data-driven clinical care.
With operations in the US and UK, Clinithink is the healthcare industry’s preferred clinical natural language processing (CNLP) provider. Clinithink’s CLiX Platform enables healthcare providers and solution vendors to extract knowledge from the clinical data trapped in the free text of discharge summaries, clinical notes, and other clinical documents. Using patent-pending algorithms and CNLP to add structure to unstructured clinical data, CLiX enables the indexing, mapping and analyzing of clinical narrative to output rich structured data encoded to medical ontologies such as SNOMED CT, ICD-9, ICD-10 and RxNorm.
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