title: Clinical information displays to improve ICU outcomes.
abstract: OBJECTIVE
  In a previous study, we compared a prototype ecological display (ED) that represented physiological data in a structured pictorial format with two bar graph displays [J.A. Effken, Improving clinical decision making through ecological interfaces, Ecol. Psych. 18 (2006) 283-318]. In ED and the first bar graph display, data were grouped hierarchically based on a cognitive work analysis (CWA); in the second bar graph display they were grouped as usually collected. Treatment efficiency (i.e., percentage of time seven variables in the CWA model were in target range) improved similarly with the two displays incorporating the CWA order for intensive care unit (ICU) residents, but not for novice ICU nurses. Hypothesized reasons for this result included: insufficient practice with novel displays; use of identical histories across displays; insufficient clinical knowledge; and the variables used in the efficiency analysis, which included only one of ED's four integrated design elements. In the current study we tested these hypotheses.
  
  
  METHODS
  We asked ICU nurses assigned to three knowledge groups based on intensive care and hemodynamic monitoring pretests to identify and treat oxygenation problems presented via ED and the first bar graph display (BGD) in an experimental laboratory simulation. We measured the impact of display, clinical scenario, data level, knowledge, presentation order, and practice extent on event recognition, treatment efficiency, cognitive workload, and user satisfaction.
  
  
  RESULTS
  The two displays produced little difference in recognition speed or overall cognitive workload, but user satisfaction was greater with ED. When 12 variables were included in the analysis, treatment efficiency improved with ED; when only 7 were measured, BGD prevailed. The results suggest benefits for the kind of synthesis provided in ED, but also a potential limitation. If too many different pictorial formats are used in a display, detecting critical events may be more difficult.
authors.affiliation: College of Nursing, The University of Arizona, Arizona, USA. jeffken@nursing.arizona.edu
authors.forename: Judith A
authors.initials: JA
authors.lastname: Effken
authors.forename: Robert G
authors.initials: RG
authors.lastname: Loeb
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Kang
authors.forename: Zu-Chun
authors.initials: ZC
authors.lastname: Lin
mesh_terms: D001292:Attitude to Computers; D001296:Audiovisual Aids / Q000592:standards; D002983:Clinical Competence; D003198:Computer Simulation; D003194:Computer-Assisted Instruction / Q000379:methods; D044962:Data Compression / Q000379:methods / Q000592:standards; D003626:Data Display / Q000592:standards / Q000706:statistics & numerical data; D020000:Decision Support Systems, Clinical / Q000592:standards*; D004526:Efficiency; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration*; D009726:Nurses / Q000523:psychology; D011214:Practice, Psychological; D013647:Task Performance and Analysis; D014584:User-Computer Interface; D000078329:Workforce