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SYNOPSIS:

With the continued adoption of electronic health record (EHR) systems, healthcare centers are developing large repositories of unstructured clinical notes that were created as part of routine care. These data contain rich details that are often found nowhere else in the EHR, and can be valuable for research tasks ranging from cohort identification and eligibility determination to extracting phenotypic details in support of clinical and translational research. However, access to the data '"locked' " within these documents has historically been challenging for research teams, many of whom lack the expertise to utilize natural language processing tools.   To address this problem we developed the Electronic Medical Record Search Engine (EMERSE) which is an information retrieval tool designed with the end-user in mind.   Careful attention has been paid to usability and to ensure that EMERSE has the type of functionality needed by a majority of researchers needing access to the data found within the clinical notes.   EMERSE has been used, and continues to be enhanced, at the University of Michigan for over 10 years, and has had a wide and highly satisfied user base. One of the largest collective user groups has been our Cancer Center's Clinical Trials Office. EMERSE is available at no cost for academic use and we are actively seeking partners interested in adopting the tool.   Additional information can be found at http://project-emerse.org.   In this talk, we will provide a live demonstration of the tool, by walking through the various features and capabilities to show the kinds of tasks it can be used for.

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