Seneca I. Perri

Evaluation of a pictograph enhancement system for patient instruction: a recall study

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). DISCUSSION 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.

A picture's meaning: The design and evaluation of pictographs illustrating patient discharge instructions

Introduction: Several studies have reported that illustrations may improve patient comprehension of health communications. However, developing effective illustrations remains a challenge. To improve discharge instructions with illustrations, we developed a computer application (Glyph) that automatically converts text to pictures using natural language processing and computer graphics techniques. Methods: We evaluated a set of pictographs created for cardiovascular discharge instructions through recognition testing, and examined illustration approaches work for specific types of medical concepts and subpopulations. We tested a large set of illustrations (n = 488) on a diverse population of subjects (n = 150) for overall recognition rates by demographic groups, representation strategies, and semantic types, as well as the effectiveness of representation strategies in relationship to recognition. Results: A majority (63%) was determined to be recognizable based on recognition scores. Results were confirmed through both descriptive statistics and a multivariable regression model analyses. Predictors of successful recognition were participant factors such as: white race, male gender, college education, and native English language. Pictograph features that predicted success included: direct representation strategy, proper level of image detail, and use in familiar contexts. Conclusion: While post-discharge care and coordination involves many complexities, the design and use of patient-centered discharge instructions may substantially impact.

Crucial issues in think aloud techniques for cross cultural studies.

Implementing user-centered design (UCD) principles and conducting usability testing is especially critical in projects where developers do not originate from the deployed environment. However, traditional usability testing techniques in cross-cultural settings can be problematic and may produce unexpected results. Measures should be taken to identify and mitigate any cultural factors that may influence UCD testing and deployment . In this paper, we focus on the think-aloud method. We use a current mHealth development and research project in Tanzania as a case study to highlight issues in UCD in cross-cultural settings. We offer recommendations for UCD methods and cultural considerations for low to middle income countries.