What Do Patients Want from Otolaryngologists? A Discrete Choice Experiment

2017 ◽  
Vol 157 (4) ◽  
pp. 618-624 ◽  
Author(s):  
Matthew R. Naunheim ◽  
Vinay K. Rathi ◽  
Margaret L. Naunheim ◽  
Blake C. Alkire ◽  
Allen C. Lam ◽  
...  

Objectives Patient preferences are crucial for the delivery of patient-centered care. Discrete choice experiments (DCEs) are an emerging quantitative methodology used for understanding these preferences. In this study, we employed DCE techniques to understand the preferences of patients presenting for an ear, nose, and throat clinic visit. Study Design DCE. Setting Decision science laboratory. Methods A DCE survey of 5 attributes—wait time, physician experience, physician personality, utilization of visit time, and cost/copayment—was constructed with structured qualitative interviews with patients. The DCE was administered to participants from the general population, who chose among hypothetical scenarios that varied across these attributes. A conditional logit model was used to determine relative attribute importance, with a separate logit model for determining subject effects. Results A total of 161 participants were included. Cost/copayment had the greatest impact on decision making (importance, 32.2%), followed by wait time and physician experience (26.5% and 24.7%, respectively). Physician personality mattered least (4.7%), although all attributes were significantly correlated to decision making. Participants preferred doctors who spent more time performing physical examination than listening or explaining. Participants were willing to pay $52 extra to avoid a 4-week delay in appointment time; $87 extra for a physician with 10 years of experience (vs 0 years); and $9 extra for a caring, friendly, and compassionate doctor (vs formal, efficient, and business-like). Conclusion DCEs allow for powerful economic analyses that may help physicians understand patient preferences. Our model showed that cost is an important factor to patients and that patients are willing to pay extra for timely appointments, experience, and thorough physical examination.

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Laura J. James ◽  
Germaine Wong ◽  
Allison Tong ◽  
Jonathan C. Craig ◽  
Kirsten Howard ◽  
...  

2017 ◽  
Vol 27 (12) ◽  
pp. 3544-3559 ◽  
Author(s):  
Anna Liza M Antonio ◽  
Robert E Weiss ◽  
Christopher S Saigal ◽  
Ely Dahan ◽  
Catherine M Crespi

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.


Trials ◽  
2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Emily Fargher ◽  
Dyfrig Hughes ◽  
Adele Ring ◽  
Ann Jacoby ◽  
Margaret Rawnsley ◽  
...  

2017 ◽  
Vol 25 (6) ◽  
pp. 578-582 ◽  
Author(s):  
Victoria Alguera-Lara ◽  
Michelle M Dowsey ◽  
Jemimah Ride ◽  
Skye Kinder ◽  
David Castle

Objectives: We reviewed the literature on shared decision making (regarding treatments in psychiatry), with a view to informing our understanding of the decision making process and the barriers that exist in clinical practice. Methods: Narrative review of published English-language articles. Results: After culling, 18 relevant articles were included. Themes identified included models of psychiatric care, benefits for patients, and barriers. There is a paucity of published studies specifically related to antipsychotic medications. Conclusions: Shared decision making is a central part of the recovery paradigm and is of increasing importance in mental health service delivery. The field needs to better understand the basis on which decisions are reached regarding psychiatric treatments. Discrete choice experiments might be useful to inform the development of tools to assist shared decision making in psychiatry.


2018 ◽  
Vol 38 (6) ◽  
pp. 658-672 ◽  
Author(s):  
Caroline Vass ◽  
Dan Rigby ◽  
Kelly Tate ◽  
Andrew Stewart ◽  
Katherine Payne

Background. Discrete choice experiments (DCEs) are increasingly used to elicit preferences for benefit-risk tradeoffs. The primary aim of this study was to explore how eye-tracking methods can be used to understand DCE respondents’ decision-making strategies. A secondary aim was to explore if the presentation and communication of risk affected respondents’ choices. Method. Two versions of a DCE were designed to understand the preferences of female members of the public for breast screening that varied in how risk attributes were presented. Risk was communicated as either 1) percentages or 2) icon arrays and percentages. Eye-tracking equipment recorded eye movements 1000 times a second. A debriefing survey collected sociodemographics and self-reported attribute nonattendance (ANA) data. A heteroskedastic conditional logit model analyzed DCE data. Eye-tracking data on pupil size, direction of motion, and total visual attention (dwell time) to predefined areas of interest were analyzed using ordinary least squares regressions. Results. Forty women completed the DCE with eye-tracking. There was no statistically significant difference in attention (fixations) to attributes between the risk communication formats. Respondents completing either version of the DCE with the alternatives presented in columns made more horizontal (left-right) saccades than vertical (up-down). Eye-tracking data confirmed self-reported ANA to the risk attributes with a 40% reduction in mean dwell time to the “probability of detecting a cancer” ( P = 0.001) and a 25% reduction to the “risk of unnecessary follow-up” ( P = 0.008). Conclusion. This study is one of the first to show how eye-tracking can be used to understand responses to a health care DCE and highlighted the potential impact of risk communication on respondents’ decision-making strategies. The results suggested self-reported ANA to cost attributes may not be reliable.


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