From data to personas: a multi-source approach to tailored online health information recommendation for ovarian cancer patients and caregivers

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ID: 332806
2026
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Ranked #69 of 88 articles by views in Journal of the American Medical Informatics Association : JAMIA

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Abstract
Abstract Objective Ovarian cancer (OvCa) patients and caregivers face complex, evolving information needs, yet digital health platforms often lack tailoring. This study developed and validated computational personas to support a tailored recommender system (Health E-Librarian with Personalized Recommendations [HELPeR]) and address the cold-start problem, in which limited information about new users reduces the system’s ability to generate relevant recommendations. Materials and Methods We used a mixed-methods design integrating 4 data sources: (1) online OvCa forum posts (n = 457; 60 users), (2) semi-structured interviews with patients and caregivers (n = 20), (3) clinical case notes from a caregiver support program, and (4) expert input from gynecologic oncology nurses. Forum data were manually annotated and analyzed using k-modes clustering to identify user subgroups. Qualitative data were analyzed using thematic analysis, and findings were integrated to construct and refine personas. Results Five distinct user clusters were identified from forum data. Integration of multi-source data yielded 15 personas (9 patients, 6 caregivers) representing trajectories from diagnosis to end-of-life care. Patient personas captured needs related to symptom management, treatment decision-making, financial concerns, and psychosocial support, while caregiver personas emphasized communication, emotional burden, self-care, and care coordination. Personas reflected dynamic, context-dependent needs and were validated for clinical realism through expert review. Discussion Persona-based modeling provides an interpretable framework for tailored recommendation and approximates digital twin approaches, helping mitigate cold-start limitations. Conclusion Multi-source persona development is a feasible strategy for enabling tailored digital health interventions and improving tailored information delivery in oncology care.
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Authors Youjia Wang, Khushboo Maulikmihir Thaker, Vivian Chi Ching Hui, Peter L. Brusilovsky, Daqing He, Heidi Scharf Donovan, Young Ji Lee
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Journal Journal of the American Medical Informatics Association : JAMIA
Year 2026
DOI
10.1093/jamia/ocag169
URL
https://doi.org/10.1093/jamia/ocag169
Keywords Keywords not found

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