Increased availability of electronic data in healthcare does not automatically warrant increased availability of information. Availability of high-end functionality does not ensure stakeholder acceptance either.

As we had discussed here before structured relational database driven applications failed to capture clinical workflow. Hence, free-text documentation is still widespread in EHR systems. However, extracting useful information out of this free form text is difficult.

This paper [ ¹ ] summarizes University of Michigan’s nine-year experience in developing and using a full-text search engine, similar to Google, designed to facilitate information retrieval (IR) from narrative documents stored in electronic health records (EHRs). The system called the Electronic Medical Record Search Engine (EMERSE), is designed specifically to support the nuances of free form medical text.

The system focuses on usability, and the acceptance has been good. EMERSE uses a variety of open-source components including Apache Lucene, Spring, Hibernate, and Apache Solr. It is vendor neutral supporting many popular EHR systems such as Epic and VistA. EMERSE is freely available from http://project-emerse.org/software.html