不同需求情景下肥胖症患者对在线医疗咨询的偏好:离散选择实验

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-11-27 DOI:10.2196/53140
Yaolin Hu, Jian Wang, Jianbo Zhou, Yuanyuan Gu, Stephen Nicholas, Elizabeth Maitland
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引用次数: 0

摘要

背景:肥胖症是一种特殊的慢性疾病,中国是世界上超重和肥胖症患者人数最多的国家。从肥胖症患者对在线医疗咨询(OMC)的需求角度进行的研究还很少。随着肥胖症在线医疗需求的不断增长,特别是由于胰高血糖素样肽-1 受体激动剂等新型药物疗法的出现,肥胖症患者正在寻求肥胖症管理建议和肥胖症药物处方。因此,我们的需求情景定义了两种控制肥胖的 OMC 动机:"目标:本研究旨在评估和比较中国肥胖症患者对 "用于药物 "和 "用于建议 "的 OMC 的偏好:根据国际药物经济学和结果研究学会的检查表,将 400 名参与者分配到 "药物 "方案和 "建议 "方案,通过离散选择实验估计 "药物 "和 "建议 "偏好。两种情景下的群体分布相似,两种不同的需求情景采用相同的离散选择实验设计,包括 16 个选择集和 6 个代表性属性。混合对数模型用于估算支付意愿和相对重要性得分:结果表明:在 "为药品 "和 "为咨询 "两种情景中,拥有知名专家和普通专家头衔的医生与普通医生;来自高级医院、省级医院、三级医院和市级医院的医生与来自较低级别的县级医院的医生;较少的等待时间;较低的OMC费用都是首选。两种方案的差异在于就诊形式、就诊时间以及就诊时间与等候时间的相对重要性。药物治疗组偏好电话咨询,而药物治疗组不偏好电话咨询;药物治疗组偏好较长的咨询时间(β=.029),而药物治疗组偏好较短的咨询时间(β=-.030);药物治疗组对咨询时间的评价高于等待时间,而药物治疗组对等待时间的评价高于咨询时间。结合我们的定性研究,这种差异可以解释为两种情况下不同的咨询需求,即 "支持药物 "组患者更喜欢较长时间的咨询,因为他们希望获得有关药物副作用的详细建议,而 "支持建议 "组参与者则更喜欢快速、直接的回复:通过揭示用户对费用、医生职称和医院级别、等待时间、咨询时间和形式的偏好,我们的研究为在线医疗平台、在线医疗监管机构和医生提供了市场细分和服务差异化战略方面的信息。
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Preferences of Individuals With Obesity for Online Medical Consultation in Different Demand Scenarios: Discrete Choice Experiments.

Background: Obesity is a unique chronic disease, with China having the largest number of people living with overweight and obesity in the world. There has been little research from the demand perspective for online medical consultation (OMC) by individuals living with obesity. With the growing demand for obesity OMC, especially due to the emergence of new pharmacotherapies, such as glucagon-like peptide-1 receptor agonists, individuals living with obesity are seeking both advice on obesity management and the prescription of obesity drugs. Therefore, our demand scenarios defined 2 OMC motivations to manage obesity: "For-Drugs" use and "For-Advice" use.

Objective: This study aims to assess and compare the preferences for For-Drugs and For-Advice OMC among individuals living with obesity in China.

Methods: Following the International Society for Pharmacoeconomics and Outcomes Research's checklist and comprising 400 participants assigned to the For-Drugs scenario and 400 to the For-Advice scenario, the For-Drugs and For-Advice preferences were estimated through discrete choice experiments. The groups in the 2 scenarios followed a similar distribution, and the 2 different demand scenarios shared the same discrete choice experiment design, comprising 16 choice sets with 6 representative attributes. Mixed logit modeling was used to estimate the willingness to pay and relative importance scores.

Results: Doctors with well-known and general expert titles, versus ordinary doctors; doctors from high-level, provincial, tertiary, and municipal hospitals, versus lower-level county hospitals; less waiting time; and lower OMC fees were preferred in both the For-Drugs and For-Advice scenarios. The differences between the 2 scenarios lay in the consultation format, consultation duration, and the relative importance of consultation duration versus waiting time. The For-Advice group preferred telephone consultations, while the For-Drugs group did not; the For-Drugs group preferred longer consultation duration (β=.029), while the For-Advice group preferred shorter consultation duration (β=-.030); and the For-Drugs group rated consultation duration higher than waiting time, while the For-Advice group rated the waiting time as more important than consultation duration. Combined with our qualitative research, the differences can be explained by the different consultation needs in the 2 scenarios, where longer patient consultations were preferred by the For-Drugs patients who sought detailed advice on drug side effects, while quick and direct responses were preferred by the For-Advice participants.

Conclusions: By revealing user preferences on costs, doctors' titles and hospital level, wait time, and consultation duration and format, our research informs OMC platforms, OMC regulators, and doctors on market segmentation and service differentiation strategies.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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