title: Providing concept-oriented views for clinical data using a knowledge-based system: an evaluation.
abstract: OBJECTIVE
  Clinical information systems typically present patient data in chronologic order, organized by the source of the information (e.g., laboratory, radiology). This study evaluates the functionality and utility of a knowledge-based system that generates concept-oriented views (organized around clinical concepts such as disease or organ system) of clinical data.
  
  
  METHODS
  The authors have developed a system that uses a knowledge base of interrelationships between medical concepts to infer relationships between data in electronic medical records. They use these inferences to produce summaries, or views, of the data that are relevant to a specific concept of interest. They evaluated the ability of the system to select relevant information, reduce information overload, and support physician information retrieval.
  
  
  METHODS
  The sensitivity and specificity of the system for identifying relevant patient information were calculated. Effect on information overload was assessed by comparing the amount of information in each view with the amount of information in the entire record. Information retrieval accuracy and cost (time) were used to measure the effect of using concept-oriented views on the efficiency and effectiveness of retrievals.
  
  
  RESULTS
  The sensitivity and specificity of the system for identifying relevant clinical information were generally in the range of 70 to 80 percent. Concept-oriented views are effective in reducing the amount of information retrieved (over 80 percent reduction) and, compared with source-oriented views, are able to improve physician retrieval accuracy (p=0.04).
  
  
  CONCLUSIONS
  Computer-generated, concept-oriented views can be used to reduce clinician information overload and improve the accuracy of clinical data retrieval.
authors.affiliation: Decision System Group, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. Qzeng@dsg.harvard.edu
authors.forename: Qing
authors.initials: Q
authors.lastname: Zeng
authors.forename: James J
authors.initials: JJ
authors.lastname: Cimino
authors.forename: Kelly H
authors.initials: KH
authors.lastname: Zou
mesh_terms: D001185:Artificial Intelligence; D015510:Clinical Medicine; D003627:Data Interpretation, Statistical; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D016347:Medical Records Systems, Computerized