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 m...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
|
| Series: | MDM Policy & Practice |
| Online Access: | https://doi.org/10.1177/23814683251328377 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849338415888203776 |
|---|---|
| author | Mary Ann E. Binuya Sabine C. Linn Annelies H. Boekhout Marjanka K. Schmidt Ellen G. Engelhardt |
| author_facet | Mary Ann E. Binuya Sabine C. Linn Annelies H. Boekhout Marjanka K. Schmidt Ellen G. Engelhardt |
| author_sort | Mary Ann E. Binuya |
| collection | DOAJ |
| description | 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. Highlights 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. |
| format | Article |
| id | doaj-art-6834701d4f7b4e46a62b04d2ed5e1e94 |
| institution | Kabale University |
| issn | 2381-4683 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | MDM Policy & Practice |
| spelling | doaj-art-6834701d4f7b4e46a62b04d2ed5e1e942025-08-20T03:44:24ZengSAGE PublishingMDM Policy & Practice2381-46832025-03-011010.1177/23814683251328377Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction ModelsMary Ann E. BinuyaSabine C. LinnAnnelies H. BoekhoutMarjanka K. SchmidtEllen G. EngelhardtBackground. 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. Highlights 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.https://doi.org/10.1177/23814683251328377 |
| spellingShingle | Mary Ann E. Binuya Sabine C. Linn Annelies H. Boekhout Marjanka K. Schmidt Ellen G. Engelhardt Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models MDM Policy & Practice |
| title | Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models |
| title_full | Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models |
| title_fullStr | Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models |
| title_full_unstemmed | Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models |
| title_short | Bridging the Gap: A Mixed-Methods Study on Factors Influencing Breast Cancer Clinicians’ Decisions to Use Clinical Prediction Models |
| title_sort | bridging the gap a mixed methods study on factors influencing breast cancer clinicians decisions to use clinical prediction models |
| url | https://doi.org/10.1177/23814683251328377 |
| work_keys_str_mv | AT maryannebinuya bridgingthegapamixedmethodsstudyonfactorsinfluencingbreastcancercliniciansdecisionstouseclinicalpredictionmodels AT sabineclinn bridgingthegapamixedmethodsstudyonfactorsinfluencingbreastcancercliniciansdecisionstouseclinicalpredictionmodels AT annelieshboekhout bridgingthegapamixedmethodsstudyonfactorsinfluencingbreastcancercliniciansdecisionstouseclinicalpredictionmodels AT marjankakschmidt bridgingthegapamixedmethodsstudyonfactorsinfluencingbreastcancercliniciansdecisionstouseclinicalpredictionmodels AT ellengengelhardt bridgingthegapamixedmethodsstudyonfactorsinfluencingbreastcancercliniciansdecisionstouseclinicalpredictionmodels |