Using Animated Computer-generated Text and Graphics to Depict the Risks and Benefits of Medical Treatment

Center for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor. Electronic address: .
The American journal of medicine (Impact Factor: 5). 08/2012; 125(11):1103-10. DOI: 10.1016/j.amjmed.2012.04.040
Source: PubMed


Conventional print materials for presenting risks and benefits of treatment are often difficult to understand. This study was undertaken to evaluate and compare subjects' understanding and perceptions of risks and benefits presented using animated computerized text and graphics.
Adult subjects were randomized to receive identical risk/benefit information regarding taking statins that was presented on an iPad (Apple Corp, Cupertino, Calif) in 1 of 4 different animated formats: text/numbers, pie chart, bar graph, and pictograph. Subjects completed a questionnaire regarding their preferences and perceptions of the message delivery together with their understanding of the information. Health literacy, numeracy, and need for cognition were measured using validated instruments.
There were no differences in subject understanding based on the different formats. However, significantly more subjects preferred graphs (82.5%) compared with text (17.5%, P<.001). Specifically, subjects preferred pictographs (32.0%) and bar graphs (31.0%) over pie charts (19.5%) and text (17.5%). Subjects whose preference for message delivery matched their randomly assigned format (preference match) had significantly greater understanding and satisfaction compared with those assigned to something other than their preference.
Results showed that computer-animated depictions of risks and benefits offer an effective means to describe medical risk/benefit statistics. That understanding and satisfaction were significantly better when the format matched the individual's preference for message delivery is important and reinforces the value of "tailoring" information to the individual's needs and preferences.

29 Reads
  • 05/2013; 167(7):1-3. DOI:10.1001/jamapediatrics.2013.152
  • [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: Evaluation of the potential usability of an iPad 3 with a high-resolution display in CT emergency diagnosis compared to a 3 D PACS workstation. Materials and methods: 3 readers used a 5-point Likert scale to evaluate 40 CCT scans and 40 CTPA scans to determine the detectability of early signs of infarction in CCT or segmental and subsegmental pulmonary embolisms in CT angiography of the pulmonary arteries (CTPA) on the iPad 3 (Apple Inc., USA) using an application for image viewing (Visage Ease, Visage Imaging GmbH, Berlin) and on a 3 D PACS workstation (Visage 7.1, Visage Imaging, Berlin) using a certified monitor for image viewing. The results were compared using the Wilcoxon rank sum test, Spearman's correlation coefficient, and a kappa statistic. Results: There was no significant difference in the median evaluations for the readings of both the CCT scans and the CTPA scans on the iPad 3 and on the workstation (p > 0.05) for all three readers. The mean Spearman's correlation coefficient for CCT and CTPA was 0.46 (± 0.2) and 0.69 (± 0.16), respectively, for the comparison iPad/PACS, 0.41 (± 0.16) and 0.68 (± 0.06), respectively, for the interobserver agreement on the iPad, and 0.35 (± 0.05) and 0.68 (± 0.10), respectively, for the interobserver agreement on the PACS. Mean kappa values for CCT of 0.52 (± 0.17) for the comparison iPad/PACS and 0.33 (± 0.16) and 0.32 (± 0.16), respectively, for the interobserver agreement on the iPad and the PACS were achieved. For CTPA average kappa values of 0.67 (± 0.19) were calculated for the comparison iPad/PACS and 0.69 (± 0.08) and 0.60 (± 0.14), respectively, for the interobserver concordance on the iPad 3 and the PACS. All differences were not statistically significant (p > 0.05). Conclusion: The variability of the interpretation of typical emergency scans on an iPad 3 with a high-resolution display and on a 3 D PACS workstation does not differ from the interobserver variability.
    RöFo - Fortschritte auf dem Gebiet der R 07/2013; 185(11). DOI:10.1055/s-0033-1350155 · 1.40 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: AimsWe aimed to develop a generic knowledge base with drug administration recommendations which allows the generation of a dynamic and comprehensive medication plan and to evaluate its comprehensibility and potential benefit in a qualitative pilot study with patients and physicians. Methods Based on a literature search and previously published medication plans, a prototype was developed and iteratively refined through qualitative evaluation (interviews with patients and focus group discussions with physicians). To develop the recommendations for safe administration of specific drugs we screened the summary of product characteristics (SmPC) of different exemplary brands, allocated the generated advice to groups with brands potentially requiring the same advice, and reviewed these allocations regarding applicability and appropriateness of the recommendations. ResultsFor the recommendations, 411 SmPCs of 140 different active ingredients including all available galenic formulations, routes of administrations except infusions, and administration devices were screened. Finally, 515 distinct administration recommendations were included in the database. In 926 different generic groups, 29 879 allocations of brands to general advice, food advice, indications, step-by-step instructions, or combinations thereof were made. Thereby, 27 216 of the preselected allocations (91.1%) were confirmed as appropriate. In total, one third of the German drug market was labelled with information. Conclusions Generic grouping of brands according to their active ingredient and other drug characteristics and allocation of standardized administration recommendations is feasible for a large drug market and can be integrated in a medication plan.
    British Journal of Clinical Pharmacology 09/2013; 76(S1). DOI:10.1111/bcp.12188 · 3.88 Impact Factor
Show more