Abstract
ObjectiveThis paper describes the University of Michigan’s nine-year experience in developing and using a full-text search engine 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), functions similar to Google but is equipped with special functionalities for handling challenges unique to retrieving information from medical text. Materials and methodsKey features that distinguish EMERSE from general-purpose search engines are discussed, with an emphasis on functions crucial to (1) improving medical IR performance and (2) assuring search quality and results consistency regardless of users’ medical background, stage of training, or level of technical expertise. ResultsSince its initial deployment, EMERSE has been enthusiastically embraced by clinicians, administrators, and clinical and translational researchers. To date, the system has been used in supporting more than 750 research projects yielding 80 peer-reviewed publications. In several evaluation studies, EMERSE demonstrated very high levels of sensitivity and specificity in addition to greatly improved chart review efficiency. DiscussionIncreased availability of electronic data in healthcare does not automatically warrant increased availability of information. The success of EMERSE at our institution illustrates that free-text EHR search engines can be a valuable tool to help practitioners and researchers retrieve information from EHRs more effectively and efficiently, enabling critical tasks such as patient case synthesis and research data abstraction. ConclusionEMERSE, available free of charge for academic use, represents a state-of-the-art medical IR tool with proven effectiveness and user acceptance.
Highlights
Background and SignificanceIn addition to improving patient care delivery, the widespread adoption of electronic health records (EHRs) in the U.S has created unprecedented opportunities for increased access to clinical data, enabling multiple secondary use purposes such as quality assurance, population health management, and clinical and translational research
Funded by the National Library of Medicine (NLM), we developed an experimental extension to Electronic Medical Record Search Engine (EMERSE) to leverage the nomenclatures included in the Unified Medical Language System® (UMLS®) Metathesaurus for more comprehensive query expansion
The most revealing result in evaluating a practical informatics tool such as EMERSE is perhaps whether people use the system in their everyday work
Summary
Background and SignificanceIn addition to improving patient care delivery, the widespread adoption of electronic health records (EHRs) in the U.S has created unprecedented opportunities for increased access to clinical data, enabling multiple secondary use purposes such as quality assurance, population health management, and clinical and translational research. The broader use of clinical data for discovery, surveillance, and improving care provides great potential to transform the U.S healthcare system into a self-learning vehicle—or a “Learning Health System”—to advance our knowledge in a wide range of clinical and policy domains.[12]. Foremost is the continued popularity of free-text documentation in EHRs. While structured data at the time of entry is desirable, unstructured clinical documentation is likely to persist due to the need by clinicians to express their thoughts in a flexible manner and to preserve the complexity and nuances of each patient.[34] Recent studies have shown that clinicians often revert to free-text entry even when coding options are provided,[] and that the free text is still needed for complex tasks such as clinical trial recruitment.[8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
