Factors Associated with Decision Aid Use in Localized Prostate Cancer.

Decision aids have been found to improve patients' knowledge of treatments and decrease decisional regrets. Despite these benefits, there is not widespread use of decision aids for newly diagnosed prostate cancer (PCa). This analysis investigates factors that impact men's choice to use a decision aid for newly diagnosed prostate cancer.

This is a retrospective analysis of a PCa registry from the Michigan Urological Surgery Improvement Collaborative (MUSIC). We included data from men with newly diagnosed, clinically localized PCa seen from 2018-21 at practices offering a PCa decision aid (Personal Patient Profile-Prostate; P3P). The primary outcome was men's registration to use P3P. We fit a multilevel logistic regression model with patient-level factors and included urologist specific random intercepts. We estimated the intra-class correlation (ICC) and predicted the probability of P3P registration among urologists.

A total of 2629 men were seen at practices that participated in P3P and 1174 (45%) registered to use P3P. Forty-one percent of the total variance of P3P registration was attributed to clustering of men under a specific urologist's care. In contrast, only 1.5% of the variance of P3P registration was explained by patient factors. Our model did not include data on socioeconomic, literacy or psychosocial factors, which limits the interpretation of the results.

These results suggest that urologists' effect far outweighs patient factors in a man's decision to enroll in P3P. Strategies that encourage providers to increase decision aid adoption in their practices are warranted.

Urology practice. 2022 Jan 01 [Epub]

Giulia I Lane, Ajith Dupati, Ji Qi, Stephanie Ferrante, Rodney L Dunn, Roshan Paudel, Daniela Wittmann, Lauren Wallner, Donna L Berry, Chad Ellimoottil, James Montie, J Quentin Clemens

Department of Urology, University of Michigan, Ann Arbor, MI, USA., Wayne State University School of Medicine, Detroit, MI, USA., Health Infrastructures and Learning Systems, University of Michigan, Ann Arbor, MI, USA., Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA., Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, USA.

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