Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians' Decisions to Use Clinical Prediction Models

Background. Clinical prediction models provide tailored risk estimates that can help guide decisions in breast cancer care. Despite their potential, few models are widely used in clinical practice. We aimed to identify the factors influencing breast cancer clinicians’ decisions to adopt prediction models and assess their relative importance. Methods. We conducted a mixed-methods study, beginning with semi-structured interviews, followed by a nationwide online survey. Thematic analysis was used to qualitatively summarize the interviews and identify key factors. For the survey, we used descriptive analysis to characterize the sample and Mann–Whitney U and Kruskal–Wallis tests to explore differences in score (0 = not important to 10 = very important) distributions. Results. Interviews (N = 16) identified eight key factors influencing model use. Practical/methodological factors included accessibility, cost, understandability, objective accuracy, actionability, and clinical relevance. Perceptual factors included acceptability, subjective accuracy, and risk communication. In the survey (N = 146; 137 model users), clinicians ranked online accessibility (median score = 9 [interquartile range = 8–10]) as most important. Cost was also highly rated, with preferences for freely available models (9 [8–10]) and those with reimbursable tests (8 [8–10]). Formal regulatory approval (7 [5–8]) and direct integration with electronic health records (6 [3–8]) were considered less critical. Subgroup analysis revealed differences in score distributions; for example, clinicians from general hospitals prioritized inclusion of new biomarkers more than those in academic settings. Conclusions. Breast cancer clinicians’ decisions to initiate use of prediction models are influenced by practical and perceptual factors, extending beyond technical metrics such as discrimination and calibration. Addressing these factors more holistically through collaborative efforts between model developers, clinicians, and communication and implementation experts, for instance, by developing clinician-friendly online tools that prioritize usability and local adaptability, could increase model uptake. Accessibility, cost, and practical considerations, such as ease of use and clinical utility, were prioritized slightly more than technical validation metrics, such as discrimination and calibration, when deciding to start using a clinical prediction model. Most breast cancer clinicians valued models with clear inputs (e.g., variable definitions, cutoffs) and outputs; few were interested in the exact model specifications. Perceptual or subjective factors, including perceived accuracy and peer acceptability, also influenced model adoption but were secondary to practical considerations. Sociodemographic variables, such as clinical specialization and hospital setting, influenced the importance of factors for model use.