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Published on: February 6, 2020
Patient- and Physician-Identified Considerations for Clinical Implementation of a New Risk-Based Prediction Tool to
Christine M Gunn1,2, Nancy Boyer3, Sidra Sheikh3
1The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH.
Purpose:
Patient, tumor, and treatment factors can help predict the chance that a woman with a history of breast cancer diagnosis will be diagnosed with a second breast cancer within a year of a negative mammogram. This qualitative study elucidates breast cancer survivor and multispecialty physician perspectives on barriers/facilitators to clinical implementation of a risk-prediction tool to support surveillance decisions.
Materials And Methods:
We enrolled women who completed primary breast cancer treatment and physicians from November 2023 to April 2024. Participants were recruited through Breast Cancer Surveillance Consortium's registries; patients participated in one of four focus groups and physicians participated in individual semistructured interviews. Participants were presented with information about an interval cancer risk prediction tool and were prompted to share perspectives on facilitators and barriers to using such a tool. To identify salient themes, thematic analysis was undertaken by three research team members.
Results:
Participants included 40 physicians and 23 patients. Three themes emerged: (1) evidence needed for tool acceptance, (2) tool features to facilitate usage, and (3) barriers to tool adoption. Both cancer survivor and physician groups were accepting of risk prediction tool use for surveillance imaging when tool development information was available; they perceived the tool would fit within workflows, and data integrity could be verified. Both groups anticipated structural (time) and technological barriers (magnetic resonance imaging availability) could impede adoption.
Conclusion:
Qualitative findings from focus groups and interviews analyzed thematically suggest implementing a risk prediction tool for surveillance imaging requires evidence transparency, health record integration, data integrity protection, and system supports to promote ease of use in clinical settings while mitigating unintended consequences. All are important to consider during tool development and implementation planning.
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