Dataset Information

Promoting Reflection on Medical Maximizing-Minimizing Preferences May Create Undesired Effects on Decisions About Low-Benefit and High-Benefit Care.

ABSTRACT:

Background

Medical maximizing-minimizing (MM) preferences predict a variety of medical decisions. We tested whether informing people about their MM preferences and asking them to reflect on the pros and cons of that preference would improve medical decisions when clear clinical recommendations exist.

Methods

We surveyed 1219 US adults age 40+ that were sampled to ensure a 50%/50% distribution of medical maximizers versus minimizers. Participants either received no MM feedback (Control) or received feedback about their MM type and instructions to reflect on how that MM type can be helpful in some circumstances and problematic in others (Reflection). All participants then completed five hypothetical decision scenarios regarding low-value care services (e.g., head computed tomography scan for mild concussion) and three about high-value care (e.g., flu vaccination).

Results

There were no significant differences between the Control and Reflection groups in five of eight scenarios. In three scenarios (two low-benefit and one high-benefit), we observed small effects in the nonhypothesized direction for the MM subgroup least likely to follow the recommendation (e.g., maximizers in the Reflection group were more likely to request low-benefit care).

Conclusions

Asking people to reflect on their MM preferences may be a counterproductive strategy for optimizing patient decision making around quality of care.

SUBMITTER: Zikmund-Fisher BJ

PROVIDER: S-EPMC7863160 | biostudies-literature | 2021 Jan-Jun

REPOSITORIES: biostudies-literature

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Publications

Promoting Reflection on Medical Maximizing-Minimizing Preferences May Create Undesired Effects on Decisions About Low-Benefit and High-Benefit Care.

Zikmund-Fisher Brian J BJ Shaffer Victoria A VA Scherer Laura D LD

MDM policy & practice 20210129 1

<h4>Background</h4>Medical maximizing-minimizing (MM) preferences predict a variety of medical decisions. We tested whether informing people about their MM preferences and asking them to reflect on the pros and cons of that preference would improve medical decisions when clear clinical recommendations exist.<h4>Methods</h4>We surveyed 1219 US adults age 40+ that were sampled to ensure a 50%/50% distribution of medical maximizers versus minimizers. Participants either received no MM feedback (Con ...[more]

PMID: 33598547

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Project description:BackgroundThe use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS) based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II), trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications.ResultsWe analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II.ConclusionsOur results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the studying endpoints. New classifiers built with MFS exhibit improved performance with both internal and external validation, suggesting that MFS method generally reduces redundancies for features within gene signatures and improves the performance of the model. Consequently, our strategy will be beneficial for the microarray-based clinical applications.

Dataset Information

Promoting Reflection on Medical Maximizing-Minimizing Preferences May Create Undesired Effects on Decisions About Low-Benefit and High-Benefit Care.

Background

Methods

Results

Conclusions

Publications

Promoting Reflection on Medical Maximizing-Minimizing Preferences May Create Undesired Effects on Decisions About Low-Benefit and High-Benefit Care.

Similar Datasets

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