title: Evaluation of a pictograph enhancement system for patient instruction: a recall study.
abstract: OBJECTIVE
  We developed a novel computer application called Glyph that automatically converts text to sets of illustrations using natural language processing and computer graphics techniques to provide high quality pictographs for health communication. In this study, we evaluated the ability of the Glyph system to illustrate a set of actual patient instructions, and tested patient recall of the original and Glyph illustrated instructions.
  
  
  METHODS
  We used Glyph to illustrate 49 patient instructions representing 10 different discharge templates from the University of Utah Cardiology Service. 84 participants were recruited through convenience sampling. To test the recall of illustrated versus non-illustrated instructions, participants were asked to review and then recall a set questionnaires that contained five pictograph-enhanced and five non-pictograph-enhanced items.
  
  
  RESULTS
  The mean score without pictographs was 0.47 (SD 0.23), or 47% recall. With pictographs, this mean score increased to 0.52 (SD 0.22), or 52% recall. In a multivariable mixed effects linear regression model, this 0.05 mean increase was statistically significant (95% CI 0.03 to 0.06, p<0.001).
  
  
  CONCLUSIONS
  In our study, the presence of Glyph pictographs improved discharge instruction recall (p<0.001). Education, age, and English as first language were associated with better instruction recall and transcription.
  
  
  CONCLUSIONS
  Automated illustration is a novel approach to improve the comprehension and recall of discharge instructions. Our results showed a statistically significant in recall with automated illustrations. Subjects with no-colleague education and younger subjects appeared to benefit more from the illustrations than others.
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Qing
authors.initials: Q
authors.lastname: Zeng-Treitler
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA College of Nursing, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Seneca
authors.initials: S
authors.lastname: Perri
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Carlos
authors.initials: C
authors.lastname: Nakamura
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Jinqiu
authors.initials: J
authors.lastname: Kuang
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA College of Nursing, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Brent
authors.initials: B
authors.lastname: Hill
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Duy Duc An
authors.initials: DD
authors.lastname: Bui
authors.affiliation: Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Stoddard
authors.affiliation: Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Bray
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D003196:Computer Graphics; D004522:Educational Status; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008488:Medical Illustration; D011939:Mental Recall; D008875:Middle Aged; D010351:Patient Discharge; D010353:Patient Education as Topic / Q000379:methods*; D012984:Software; D055815:Young Adult
keywords: consumer health informatics; discharge instructions; evaluation; patient education; pictographs