DEDUCE Clinical Text: An Ontology-based Module to Support Self-Service Clinical Notes Exploration and Cohort Development

Large amounts of information, as well as opportunities for informing research, education, and operations, are contained within clinical text such as radiology reports and pathology reports. However, this content is less accessible and harder to leverage than structured, discrete data. We report on an extension to the Duke Enterprise Data Unified Content Explorer (DEDUCE), a self-service query tool developed to provide clinicians and researchers with access to data within the Duke Medicine Enterprise Data Warehouse (EDW). The DEDUCE Clinical Text module supports ontology-based text searching, enhanced filtering capabilities based on document attributes, and integration of clinical text with structured data and cohort development. The module is implemented with open-source tools extensible to other institutions, including a Java-based search engine (Apache Solr) with complementary full-text indexing library (Lucene) employed with a negation engine (NegEx) modified by clinical users to include to local domain-specific negation phrases.