Engaging patients in complex clinical decision-making: Successes, pitfalls, and future directions

Laviana, Aaron A.; Pannell, Stephanie C.; Huen, Kathy H.; Bergman, Jonathan

doi:10.1016/j.urolonc.2017.07.014

Cited by 4 publications

(9 citation statements)

References 45 publications

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“…However, Laviana et al found that in most tumour groups, patients do not receive sufficient information to educate them on their treatment options 64 . They recommended that structured decision aids aimed at guiding and supporting the patient through this process, ultimately reduce anxiety 64 and improve concordance between patient and clinician 65,66 . Given that anxiety has been identified as a barrier in our review, implementing such decision aids will support the decision-making process.…”

Section: Patient-related Criteriamentioning

confidence: 99%

The Current Evidence for Factors that Influence Treatment Decision Making in Localized Kidney Cancer: A Mixed Methods Systematic Review

et al. 2021

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Introduction: With a growing number of treatment options for localised kidney cancer, patients and health care professionals have both the opportunity and the burden of selecting the most suitable management option. This mixed method systematic review aims to understand the barriers and facilitators of the treatment decision-making process in localised kidney cancer. Methods: We searched PubMed, Embase, and Cochrane Central databases between 1st of January 2004 and 23rd of April 2020 using the JBI Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Review and Meta-analysis statement. We identified 553 unique citations, of these 511 were excluded resulting in 42 articles included for synthesis. The Purpose, Respondents, Explanation, Findings and Significance and the Strengthening the Reporting of Observational Studies in Epidemiology checklist were applied. Results: The key themes describing barriers and facilitators to treatment decisionmaking were identified and categorised into three domains: (1) kidney cancer specific characteristics; (2) decision-maker related criteria; and (3) contextual factors.The main facilitators identified within these domains were: size at diagnosis, age, comorbidities, BMI, gender, nephrometry scoring systems, biopsy, socio-economic status, family history of cancer, year of diagnosis, geographic region and practice pattern. The key barriers were race, gender, patient anxiety, low confidence in diagnostic and treatment options, cost of procedure and practice patterns.

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Section: Patient-related Criteriamentioning

confidence: 99%

The Current Evidence for Factors that Influence Treatment Decision Making in Localized Kidney Cancer: A Mixed Methods Systematic Review

et al. 2021

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“…While appealing to healthcare suppliers to undertake their tasks, a realistic problem should be noticed. Without an easy approach to find the optimal‐threshold, it is very difficult for healthcare suppliers to achieve, measure, and synthesize all the available information (both objective and subjective) for a correct health decision …”

Section: Introductionmentioning

confidence: 99%

“…It is optimal for medical decision making. Using the idea of probability threshold in decision curve analysis (DCA), where the expected benefit of undergoing subsequent therapy (or further tests) is equal to the expected benefit of refraining, net benefit can be easily calculated and compared, without complex and confusing questionnaire …”

Section: Introductionmentioning

confidence: 99%

“…Without an easy approach to find the optimal-threshold, it is very difficult for healthcare suppliers to achieve, measure, and synthesize all the available information (both objective and subjective) for a correct health decision. 1,3,11 Traditional Youden's index is widely used for optimal-threshold determination. [12][13][14] However, the definition of the index indicates that it is merely associated with the test sensitivity and specificity.…”

Section: Introductionmentioning

confidence: 99%

“…Using the idea of probability threshold in decision curve analysis (DCA), where the expected benefit of undergoing subsequent therapy (or further tests) is equal to the expected benefit of refraining, [15][16][17] net benefit can be easily calculated and compared, without complex and confusing questionnaire. 7,11,18 In this study, we attempted to combine the net benefit with Youden's index. Therefore, the purpose of Youden's index was expanded from pursuing the best test accuracy to pursuing the best net benefit.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Youden's index with net benefit: A feasible approach for optimal‐threshold determination in shared decision making

Lin

Zou

2019

Evaluation Clinical Practice

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Objectives To establish an enhanced Youden's index with net benefit, as a scientific method for optimal‐threshold determination in shared decision making. Method We redefined the Youden's index by changing the target function from the maximization of accuracy to the maximization of net benefit and established the process for pursuing the patient's individual optimal‐threshold. In addition, we illustrated how the enhanced Youden's index works with an example of cataract operation decision making using the data from Shanghai, China. Results We modified the method of calculating net benefit in decision curve analysis and fused it with Youden's index to expand the purpose from finding a threshold with the best test accuracy to finding one with the best net benefit. As an example, we used the enhanced Youden's index to establish an optimal‐thresholds list for cataract operation decision making. Then, we calculated three imaginary patients’ respective thresholds after integrating the local recovery rate, their visual activities, and individual preferences. The results demonstrated that the thresholds of patients with the same objective examination results might vary widely due to their individual preferences. Therefore, the thresholds are more individualized according to the enhanced Youden's index. Conclusion The enhanced Youden's index can establish the optimal‐thresholds from the perspective of maximization of patients' net benefit and provide a quantifiable method for shared decision making.

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Trends in the Cost of Cancer Care: Beyond Drugs

Laviana

Luckenbaugh

Resnick

2020

JCO

Self Cite

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Engaging patients in complex clinical decision-making: Successes, pitfalls, and future directions

Cited by 4 publications

References 45 publications

The Current Evidence for Factors that Influence Treatment Decision Making in Localized Kidney Cancer: A Mixed Methods Systematic Review

The Current Evidence for Factors that Influence Treatment Decision Making in Localized Kidney Cancer: A Mixed Methods Systematic Review

Enhanced Youden's index with net benefit: A feasible approach for optimal‐threshold determination in shared decision making

Trends in the Cost of Cancer Care: Beyond Drugs

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