Design of patient-facing immunization visualizations affects task performance: an experimental comparison of 4 electronic visualizations.
- Authors
- Type
- Published Article
- Journal
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press
- Publication Date
- Nov 01, 2024
- Volume
- 31
- Issue
- 11
- Pages
- 2429–2439
- Identifiers
- DOI: 10.1093/jamia/ocae125
- PMID: 38833256
- Source
- Medline
- Keywords
- Language
- English
- License
- Unknown
Abstract
This study experimentally evaluated how well lay individuals could interpret and use 4 types of electronic health record (EHR) patient-facing immunization visualizations. Participants (n = 69) completed the study using a secure online survey platform. Participants viewed the same immunization information in 1 of 4 EHR-based immunization visualizations: 2 different patient portals (Epic MyChart and eClinicWorks), a downloadable EHR record, and a clinic-generated electronic letter (eLetter). Participants completed a common task, created a standard vaccine schedule form, and answered questions about their perceived workload, subjective numeracy and health literacy, demographic variables, and familiarity with the task. The design of the immunization visualization significantly affected both task performance measures (time taken to complete the task and number of correct dates). In particular, those using Epic MyChart took significantly longer to complete the task than those using eLetter or eClinicWorks. Those using Epic MyChart entered fewer correct dates than those using the eLetter or eClinicWorks. There were no systematic statistically significant differences in task performance measures based on the numeracy, health literacy, demographic, and experience-related questions we asked. The 4 immunization visualizations had unique design elements that likely contributed to these performance differences. Based on our findings, we provide practical guidance for the design of immunization visualizations, and future studies. Future research should focus on understanding the contexts of use and design elements that make tables an effective type of health data visualization. © The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.