澳大利亚消费者对医疗预约提醒的偏好:离散选择实验

Shayma Mohammed Selim, Sameera Senanayake, Steven M. McPhail, Hannah E. Carter, Sundresan Naicker, Sanjeewa Kularatna
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引用次数: 0

摘要

背景在确定使用哪些策略来提高患者的预约就诊率时,考虑消费者的偏好及其具体需求的证据是非常重要的。受访者被要求在三个选项中做出选择:首选提醒系统(A 或 B)或 "两者皆非 "选项。通过文献综述和专家小组讨论确定了选项的属性。提醒选项由四个属性定义:方式、时间、内容和互动性。多项式对数模型和混合多项式对数模型用于估算个人对这些属性的偏好。结果受访者(n = 361)对通过短信发送预约提醒有明显偏好(β = 2.42,p < 0.001)、包括预约费用在内的基本详情(β = 0.13,p < 0.10)、可取消或修改预约(β = 1.36,p < 0.001)。我们的研究结果为医疗消费者如何权衡提醒系统的不同特性提供了证据,这可能对当前或未来的系统有参考价值。未来的研究可能会侧重于探索患者首选提醒系统与医疗服务提供者使用的其他缓解策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

Background

It is essential to consider the evidence of consumer preferences and their specific needs when determining which strategies to use to improve patient attendance at scheduled healthcare appointments.

Objectives

This study aimed to identify key attributes and elicit healthcare consumer preferences for a healthcare appointment reminder system.

Methods

A discrete choice experiment was conducted in a general Australian population sample. The respondents were asked to choose between three options: their preferred reminder (A or B) or a ‘neither’ option. Attributes were developed through a literature review and an expert panel discussion. Reminder options were defined by four attributes: modality, timing, content and interactivity. Multinomial logit and mixed multinomial logit models were estimated to approximate individual preferences for these attributes. A scenario analysis was performed to estimate the likelihood of choosing different reminder systems.

Results

Respondents (n = 361) indicated a significant preference for an appointment reminder to be delivered via a text message (β = 2.42, p < 0.001) less than 3 days before the appointment (β = 0.99, p < 0.001), with basic details including the appointment cost (β = 0.13, p < 0.10), and where there is the ability to cancel or modify the appointment (β = 1.36, p < 0.001). A scenario analysis showed that the likelihood of choosing an appointment reminder system with these characteristics would be 97%.

Conclusions

Our findings provide evidence on how healthcare consumers trade-off between different characteristics of reminder systems, which may be valuable to inform current or future systems. Future studies may focus on exploring the effectiveness of using patient-preferred reminders alongside other mitigation strategies used by providers.

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