Optimizing a Decision Aid for Breast Cancer Radiotherapy: Accessibility and Patient Preferences
Shared decision-making (SDM)—a collaborative process in which patients and healthcare providers (HCPs) make treatment decisions together based on medical evidence and patient preferences—is a cornerstone of patient-centered care.
To support SDM in breast cancer radiotherapy, Maastro developed the BRASA patient decision aid, which offers evidence-based information and helps patients reflect on how treatment options may affect their quality of life and align with their personal values.
Although BRASA is in use, further refinement is needed to ensure it fully meets patients’ needs, particularly regarding understandability, accessibility, and the integration of emerging technologies such as AI-based predictions and “patients-like-me” dashboards. Currently, we are working on several projects focused on optimizing these aspects to enhance patient engagement, adapt to user preferences, and promote inclusivity.
Research questions:
- How do patients perceive AI-generated predictions in patient decision aids?
- What are the barriers and facilitators for implementing a ‘patients-like-me’ dashboard for breast cancer?
- How can the BRASA tool be optimized to support patients with varying levels of health literacy?
Research design:
- Qualitative interview study
- Mixed-methods study using quantitative questionnaire data and qualitative interviews.
Methods for data collection: Semi-structured interviews & Questionnaires
Potential supervisor: Madeline Therrien, PhD student at Maastro (madeline.therrien@maastro.nl)
Required skills:
- English
- Experience or interest in qualitative interviews and analysis
- Experience or interest in mixed-methods research
Time period: tbd
Location: Clinical Data Science (CDS), Faculty of Science and Engineering (FSE), Maastricht University.