Background: Women of low socioeconomic status (SES) diagnosed with early stage breast cancer are less likely to be involved in treatment decisions. They tend to report higher decisional regret and poorer communication. Evidence suggests that well-designed encounter decision aids (DAs) could improve outcomes and potentially reduce healthcare disparities. Our goal was to evaluate the acceptability and feasibility of encounter decision aids (Option Grid, Comic Option Grid, and Picture Option Grid) adapted for a low-SES and low-literacy population. Methods: We used a multi-phase, mixed-methods approach. In phase 1, we conducted a focus group with rural community stakeholders. In phase 2, we developed and administered a web-based questionnaire with patients of low and high SES. In phase 3, we interviewed patients of low SES and relevant healthcare professionals. Results: Data from phase 1 (n = 5) highlighted the importance of addressing treatment costs for patients. Data from phase 2 (n = 268) and phase 3 (n = 15) indicated that using both visual displays and numbers are helpful for understanding statistical information. Data from all three phases suggested that using plain language and simple images (Picture Option Grid) was most acceptable and feasible. The Comic Option Grid was deemed least acceptable. Conclusion: Option Grid and Picture Option Grid appeared acceptable and feasible in facilitating patient involvement and improving perceived understanding among patients of high and low SES. Picture Option Grid was considered most acceptable, accessible and feasible in the clinic visit. However, given the small sample sizes used, those findings need to be interpreted with caution. Further research is needed to determine the impact of pictorial and text-based encounter decision aids in underserved patients and across socioeconomic strata.
CITATION STYLE
Alam, S., Elwyn, G., Percac-Lima, S., Grande, S., & Durand, M. A. (2016). Assessing the acceptability and feasibility of encounter decision AIDS for early stage breast cancer targeted at underserved patients. BMC Medical Informatics and Decision Making, 16(1), 1–13. https://doi.org/10.1186/s12911-016-0384-2
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