title: The granularity of medical narratives and its effect on the speed and completeness of information retrieval.
abstract: OBJECTIVE
  Using electronic rather than paper-based record systems improves clinicians' information retrieval from patient narratives. However, few studies address how data should be organized for this purpose. Information retrieval from clinical narratives containing free text involves two steps: searching for a labeled segment and reading its content. The authors hypothesized that physicians can retrieve information better when clinical narratives are divided into many small, labeled segments ("high granularity").
  
  
  METHODS
  The study tested the ability of 24 internists and 12 residents at a teaching hospital to retrieve information from an electronic medical record--in terms of speed and completeness--when using different granularities of clinical narratives. Participants solved, without time pressure, predefined problems concerning three voluminous, inpatient case records. To mitigate confounding factors, participants were randomly allocated to a sequence that was balanced by patient case and learning effect.
  
  
  RESULTS
  Compared with retrieval from undivided notes, information retrieval from problem-partitioned notes was 22 percent faster (statistically significant), whereas retrieval from notes divided into organ systems was only 11 percent faster (not statistically significant). Subdividing segments beyond organ systems was 13 percent slower (statistically significant) than not subdividing. Granularity of medical narratives affected the speed but not the completeness of information retrieval.
  
  
  CONCLUSIONS
  Dividing voluminous free-text clinical narratives into labeled segments makes patient-related information retrieval easier. However, too much subdivision slows retrieval. Study results suggest that a coarser granularity is required for optimal information retrieval than for structured data entry. Validation of these conclusions in real-life clinical practice is recommended.
authors.affiliation: Department of Medical Informatics, Maastricht University, The Netherlands. huibert.tange@mi.unimaas.nl
authors.forename: H J
authors.initials: HJ
authors.lastname: Tange
authors.forename: H C
authors.initials: HC
authors.lastname: Schouten
authors.forename: A D
authors.initials: AD
authors.lastname: Kester
authors.forename: A
authors.initials: A
authors.lastname: Hasman
mesh_terms: D018592:Cross-Over Studies; D005069:Evaluation Studies as Topic; D006784:Hospitals, Teaching; D006801:Humans; D016247:Information Storage and Retrieval; D007388:Internal Medicine; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D008501:Medical Records, Problem-Oriented; D009426:Netherlands; D011897:Random Allocation