Patient and Physician Perspectives on Using Risk Prediction to Support Breast Cancer Surveillance Decision Making

Christine M. Gunn, Nancy Boyer, Sidra Sheikh, Janie M. Lee, Steven Woloshin, Jennifer M. Specht, Rebecca A. Hubbard, Erin J. Aiello Bowles, Yu-Ru Su and Anna N. A. Tosteson
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Christine M. Gunn: The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
Nancy Boyer: Center for Program Design and Evaluation, The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
Sidra Sheikh: Center for Program Design and Evaluation, The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
Janie M. Lee: University of Washington, Department of Radiology and Fred Hutchinson Cancer Center
Steven Woloshin: The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
Jennifer M. Specht: University of Washington, Division of Hematology and Oncology, Fred Hutch Cancer Center, Clinical Research Division
Rebecca A. Hubbard: Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
Erin J. Aiello Bowles: Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
Yu-Ru Su: Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
Anna N. A. Tosteson: The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA

Medical Decision Making, 2026, vol. 46, issue 1, 35-46

Abstract: Introduction Breast cancer survivors have a higher risk of interval cancers relative to the screening population. Patient characteristics including features of the primary cancer and its treatment can help predict interval second breast cancer risk, but patient and physician perspectives on how risk prediction tools might enhance surveillance decision making are not well characterized. Design We conducted a qualitative study of women with breast cancer who had completed primary treatment and multispecialty physicians recruited through Breast Cancer Surveillance Consortium registries. We conducted semi-structured focus groups with 5 to 7 breast cancer survivors and individual physician interviews. All participants were presented with information about an interval cancer risk prediction tool. We elicited participant perspectives on aspects of the tool’s design, relevance, and use for surveillance decision making. Data coding, thematic analysis, and interpretation were guided by the principles of theoretical thematic analysis. Results Forty physician interviews and 4 focus groups involving 23 breast cancer survivors were analyzed. Two prominent areas of focus emerged: 1) perspectives on how a risk prediction tool would enhance and add value to patient-centered care and 2) risk prediction tools can be a means to improve communication about risk of in-breast recurrence or new breast cancer. Conclusions This study provides data on breast cancer survivor and physician perceptions of a new risk prediction tool to support surveillance imaging decisions among breast cancer survivors. Implications An interval second breast cancer risk prediction tool may promote evidence-based care across an array of physicians and different clinical settings. Future research should identify care delivery settings and features that promote adoption and support use in ways that improve shared decision making and patient outcomes. Highlights This qualitative study of breast cancer survivors and physicians found that risk prediction tools to support surveillance decisions were perceived positively when positioned as a supplement to the patient–physician relationship. Both patients and physicians said that a tool supported by strong evidence and accessible outputs would be valuable for shared decision making.

Keywords: breast cancer; mammography; risk prediction; shared decision making; surveillance; survivorship (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:46:y:2026:i:1:p:35-46

DOI: 10.1177/0272989X251379888

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