title: Visual clustering analysis of CIS logs to inform creation of a user-configurable Web CIS interface.
abstract: BACKGROUND
  In this paper, we describe a new method for the study of clinical information system (CIS) logfiles joined with information in the clinical data warehouse. This method uses heatmap representations and clustering techniques to examine clinicians' viewing patterns of laboratory test results. The context of our application of these techniques is to inform the creation of a widget-based interface to the CIS.
  
  
  OBJECTIVE
  We address the rationale, feasibility, and usefulness of our method through examination of three hypotheses: 1) The frequency distribution of laboratory test viewing will follow a 'long tail' pattern, indicating that patterns are highly variable and supporting the rationale for a widget-based configurable system. 2) Patterns of laboratory testing viewing (by clinician, specialty, clinician/patient/day, and ICD-9-CM codes) can be distinguished by our methods. 3) The identified clusters will include more than 80% of the laboratory test elements found in 30 randomly selected patient records for one day.
  
  
  METHODS
  The data were plotted as heatmaps and clustered using hierarchical clustering software. Various parameters were tested to give the optimal clusters.
  
  
  RESULTS
  All the hypotheses were supported. For Hypothesis 3, 91.4% of information elements in the records were covered by the generated clusters.
  
  
  CONCLUSIONS
  Study findings support the rationale, feasibility, and usefulness of our methods to examine patterns of information access among clinicians and to inform the creation of widget-based interfaces. The results also contribute to our general understanding of clinicians' CIS use.
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 West 168th St. Floor VC5, New York, NY 10032, USA. yalini@dbmi.columbia.edu
authors.forename: Y
authors.initials: Y
authors.lastname: Senathirajah
authors.forename: S
authors.initials: S
authors.lastname: Bakken
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D005240:Feasibility Studies; D006801:Humans; D008328:Man-Machine Systems; D061108:Social Media / Q000639:trends*; D012984:Software; D014584:User-Computer Interface; D014785:Vision, Ocular