title: Automated methods for the summarization of electronic health records.
abstract: OBJECTIVE
  This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation.
  
  
  BACKGROUND
  The target audience for this review includes researchers, designers, and informaticians who are concerned about the problem of information overload in the clinical setting as well as both users and developers of clinical summarization systems.
  
  
  METHODS
  Automated summarization has been a long-studied subject in the fields of natural language processing and human-computer interaction, but the translation of summarization and visualization methods to the complexity of the clinical workflow is slow moving. We assess work in aggregating and visualizing patient information with a particular focus on methods for detecting and removing redundancy, describing temporality, determining salience, accounting for missing data, and taking advantage of encoded clinical knowledge. We identify and discuss open challenges critical to the implementation and use of robust EHR summarization systems.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA rp2521@cumc.columbia.edu.
authors.forename: Rimma
authors.initials: R
authors.lastname: Pivovarov
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, USA.
authors.forename: Noémie
authors.initials: N
authors.lastname: Elhadad
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing
keywords: Clinical summarization; electronic health records; missing data; natural language processing; semantic similarity; temporality