title: Supporting information retrieval from electronic health records: A report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE).
abstract: OBJECTIVE
  This 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.
  
  
  METHODS
  Key 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.
  
  
  RESULTS
  Since 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.
  
  
  CONCLUSIONS
  Increased 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.
  
  
  CONCLUSIONS
  EMERSE, available free of charge for academic use, represents a state-of-the-art medical IR tool with proven effectiveness and user acceptance.
authors.affiliation: Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, USA; School of Information, University of Michigan, Ann Arbor, MI, USA. Electronic address: hanauer@med.umich.edu.
authors.forename: David A
authors.initials: DA
authors.lastname: Hanauer
authors.affiliation: School of Information, University of Michigan, Ann Arbor, MI, USA; Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Qiaozhu
authors.initials: Q
authors.lastname: Mei
authors.affiliation: Medical Center Information Technology, University of Michigan Health System, Ann Arbor, MI, USA.
authors.forename: James
authors.initials: J
authors.lastname: Law
authors.affiliation: Medical Center Information Technology, University of Michigan Health System, Ann Arbor, MI, USA.
authors.forename: Ritu
authors.initials: R
authors.lastname: Khanna
authors.affiliation: School of Information, University of Michigan, Ann Arbor, MI, USA; School of Public Health, Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Zheng
mesh_terms: D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D062527:Meaningful Use; D008824:Michigan; D009323:Natural Language Processing; D057386:Search Engine; D012984:Software; D012986:Software Validation
keywords: Electronic health records (E05.318.308.940.968.625.500); Information storage and retrieval (L01.470); Search engine (L01.470.875)