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Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
Published on: February 6, 2020
Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians' Decisions to Use Clinical
Mary Ann E Binuya1,2,3, Sabine C Linn1,4,5, Annelies H Boekhout6
1Division of Molecular Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
MDM Policy & Practice
|March 28, 2025
Summary
Breast cancer clinicians prioritize practical factors like accessibility and cost over technical accuracy when adopting clinical prediction models. Addressing these usability needs can improve model uptake in practice.
Area of Science:
- Oncology
- Medical Informatics
- Health Services Research
Background:
- Clinical prediction models offer personalized risk estimates for breast cancer care decisions.
- Widespread adoption of these models in clinical practice remains limited.
- Understanding clinician adoption barriers is crucial for effective implementation.
Purpose of the Study:
- To identify and assess the importance of factors influencing breast cancer clinicians' decisions to adopt prediction models.
- To explore the relative impact of practical, methodological, and perceptual factors on model use.
Main Methods:
- A mixed-methods approach combining semi-structured interviews and a nationwide online survey.
- Thematic analysis of qualitative interview data to identify key influencing factors.
- Descriptive and inferential statistical analyses (Mann-Whitney U, Kruskal-Wallis tests) of survey data from 146 clinicians.
Main Results:
- Clinicians prioritized online accessibility (median=9) and cost (free models, reimbursable tests) over technical metrics.
- Practical factors (accessibility, cost, understandability, clinical relevance) and perceptual factors (acceptability, perceived accuracy) were key.
- Formal regulatory approval and EHR integration were less critical; sociodemographic factors influenced priorities.
Conclusions:
- Clinician adoption of breast cancer prediction models hinges on practical and perceptual factors beyond technical performance.
- A holistic approach involving developers, clinicians, and implementation experts is needed.
- Developing user-friendly, adaptable online tools can enhance model uptake and clinical utility.
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