Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Objective: To evaluate the association between self-perceived use of shared decision-making among urologists with use of validated prediction tools and self-described surgical decision-making.

Methods: This is a convergent mixed methods study of these parallel data from two modules (Shared Decision Making and Validated Prediction tools) within the 2019 American Urological Association (AUA) Annual Census. The shared decision-making (SDM) module queried aspects of SDM that urologists regularly used. The validated prediction tools module queried whether urologists regularly used, trusted, and found prediction tools helpful. Selected respondents to the 2019 AUA Annual Census underwent qualitative interviews on their surgical decision-making.

Results: In the weight sampled of 12,312 practicing urologists, most (77%) reported routine use of SDM, whereas only 30% noted regular use of validated prediction tools. On multivariable analysis, users of prediction tools were not associated with regular SDM use (31% vs 28%, P = .006) though was associated with use of decision aids f (32% vs 26%, P < .001). Shared decision-making emerged thematically with respect to matching treatment options, prioritizing goals, and navigating challenging decisions. However, the six specific components of shared decision-making ranged in their mentions within qualitative interviews.

Conclusion: Most urologists report performing SDM as supported by its thematic presence in surgical decision-making. However, only a minority use validated prediction tools and urologists infrequently mention specific SDM components. This discrepancy provides an opportunity to explore how urologists perform SDM and can be used to support integrated strategies to implement SDM more effectively in clinical practice.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.urology.2023.10.026DOI Listing

Publication Analysis

Top Keywords

prediction tools
24
validated prediction
16
shared decision-making
12
aua annual
8
annual census
8
module queried
8
urologists regularly
8
prediction
6
tools
6
urologists
5

Similar Publications

Predicting Unplanned Readmission Risk in Patients With Cirrhosis: Complication-Aware Dynamic Classifier Selection Approach.

JMIR Med Inform

September 2025

College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.

Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.

View Article and Find Full Text PDF

The differential diagnosis within polyuria-polydipsia syndrome, especially in the pediatric population, remains challenging. Despite its limited accuracy, the water deprivation test (WDT) is the reference test in pediatrics. We retrospectively analyzed performed in 65 pediatric patients (mean age 8.

View Article and Find Full Text PDF

Predicting the future risk and outcomes of severe heart failure and coronary artery disease with machine learning in the UK Biobank Cohort.

PLoS One

September 2025

Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.

Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.

Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.

View Article and Find Full Text PDF

BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disorder. While AD diagnosis traditionally relies on clinical criteria, recent trends favor a precise biological definition. Existing biomarkers efficiently detect AD pathology but inadequately reflect the extent of cognitive impairment or disease heterogeneity.

View Article and Find Full Text PDF

Importance: Increasingly, strategies to systematically detect melanomas invoke targeted approaches, whereby those at highest risk are prioritized for skin screening. Many tools exist to predict future melanoma risk, but most have limited accuracy and are potentially biased.

Objectives: To develop an improved melanoma risk prediction tool for invasive melanoma.

View Article and Find Full Text PDF