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

Abstract

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.

Keywords:

• Discharge
• Patient
• Evaluation
• Informatics
• Medical illustration
• Patient education

Acknowledgment

This work was supported by NIH grant R01 LM07222.

Disclaimer statements

Contributors The Journal of Communication in Healthcare: strategies, media, and engagement in global health makes a distinction between those who should be cited as authors, and those who have contributed to the paper but perhaps not taken such a full role.

Funding None.

Conflict of interest No conflicts of interest exist among any authors of this research manuscript at the time of submission.

Ethics approval The University of Utah Institutional Review Board (IRB) approved this study.

Additional information

Notes on contributors

Seneca Perri

Seneca Perri has a PhD in nursing informatics from the University of Utah with an emphasis on electronic medical decision support and health literacy for low-resource populations.

Lauren Argo

Lauren Argo studied interaction design at Savannah College of Art and Design and received a BFA in industrial design.

Jinqiu Kuang

Jinqiu Kuang Jinqiu has a masters’ degree in biomedical informatics at the University of Utah. She has extensive experience working in consumer health vocabularies and using pictograph enhanced discharge instruction to improve patient recall and satisfaction.

Duy Bui

Duy Bui is a PhD student at the University of Utah.

Brent Hill

Brent Hill is an RN and has his PhD in nursing informatics from the University of Utah. He has extensive experience in cardiology and developing discharge instructions.

Bruce Bray

Bruce E. Bray is a cardiologist and expert in Internet tools in the health care environment.

Qing Treitler-Zeng

Qing T. Zeng earned her PhD degree in medical informatics from Columbia University. She is a professor in the University of Utah's Department of Biomedical Informatics, and an IDEA Center Principal Investigator at the University of Utah and the VA Hospital.

Her expertise is in clinical and consumer health information extraction, analysis, and presentation.