谷歌浏览器插件
订阅小程序
在清言上使用

Collecting Physicians' Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopedists Using 2 Different Stated Preference Methods.

Medical decision making(2023)

引用 0|浏览16
暂无评分
摘要
Objectives. Physician preference items (PPIs) are high-cost medical devices for which clinicians express firm preferences with respect to a particular manufacturer or product. This study aims to identify the most important factors in the choice of new PPIs (hip or knee prosthesis) and infer about the existence of possible response biases in using 2 alternative stated preference techniques. Methods. Six key attributes with 3 levels each were identified based on a literature review and clinical experts' opinions. An online survey was administered to Italian hospital orthopedists using type 1 best-worst scaling (BWS) and binary discrete choice experiment (DCE). BWS data were analyzed through descriptive statistics and conditional logit model. A mixed logit regression model was applied to DCE data, and willingness-to-pay (WTP) was estimated. All analyses were conducted using Stata 16. Results. A sample of 108 orthopedists were enrolled. In BWS, the most important attribute was "clinical evidence," followed by "quality of products," while the least relevant items were "relationship with the sales representative" and "cost." DCE results suggested instead that orthopedists prefer high-quality products with robust clinical evidence, positive health technology assessment recommendation and affordable cost, and for which they have a consolidated experience of use and a good relationship with the sales representative. Conclusions. The elicitation of preferences for PPIs using alternative methods can lead to different results. The BWS of type 1, which is similar to a ranking exercise, seems to be more affected by acquiescent responding and social desirability than the DCE, which introduces tradeoffs in the choice task and is likely to reveal more about true preferences.
更多
查看译文
关键词
physician preference items,discrete choice experiment,best-worst scaling,response bias,preference elicitation,health technology assessment,orthopaedics,Italy
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要