Who is your ideal peer mentor? A qualitative study to identify cancer patient preferences for a digital peer support app
Image credit: John KildeaAbstract
Purpose
Peer support can provide many benefits to cancer patients. However, sustained use of one-on-one peer support requires a good match between patient and peer mentor. Using an artificial intelligence (AI) matching algorithm has the potential to improve peer matching by achieving complex, preference-based matching. Therefore, using stakeholder co-design, this study identified patient preferences for peer matching criteria and other features of OpalBuddy, a digital peer support service to be developed within the Opal patient portal.
Methods
Patients using the Opal app were recruited, and semi-structured individual interviews were carried out with eight available women cancer patients. Qualitative data analysis followed an iterative and collaborative thematic analysis approach, using computer-assisted software (NVivo).
Results
Three themes, with supporting sub-themes, that describe patient preferences for matching with an ideal peer mentor were identified:
Theme 1. An ideal mentor can provide support at multiple levels, with sub-themes describing the levels: (A) Sharing illness experiences, (B) Practical information support, (C) Emotional support, (D) Social management coaching.
Theme 2. The ideal mentor has similar lived experience, with sub-themes describing the type of lived experience: (A) Similar clinical situation, (B) Similar socio-demographics, (C) Interpersonal affinity.
Theme 3. The ideal peer mentor will be supported in their role, with sub-themes describing support options: (A) Formal or informal training, (B) General guidance, (C) Supportive supervision.
Finally, based on different support needs (practical vs emotional), it was found that patients had varying, even opposing, expectations from a mentor’s interpersonal communication style (solution focused vs good listener).
Conclusion
Patient preferences for an ideal peer mentor were identified through semi-structured interviews with a sample of eight women with a diverse set of cancers. Findings will be used to guide further work, including a similar study with men and a pilot study of a digital patient matching service for peer support in the open-source Opal patient portal.
Kelly Agnew
Data Scientist
Douaa El Abiad
Research Assistant
Luc Galarneau
Research Associate
Susie Judd
DevSecOps Manager
James Manalad
PhD Student, Former MSc Student
Ridhi Mittal
Undergraduate Student
Tristan Williams
Social Media Coordinator, Patient Partner
Angele Wen
Former Laurie J. Hendren Scholars
