Developing a Framework for Online Review-Based Health Care Service Quality Assessment: Text-Mining Study.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Xue Zhang, Jianshan Sun, Xin Li, Yezheng Liu, Chenwei Li
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引用次数: 0

Abstract

Background: With the development of online health care platforms, patient reviews have become an important source for assessing medical service quality. However, the critical aspects of quality dimensions in textual reviews remain largely unexplored.

Objective: This study aims to establish a comprehensive medical service quality assessment framework by leveraging online review data. Such a framework would support large service providers, such as online platforms, to assess the quality of many doctors efficiently.

Methods: We adopted a text-mining approach with theory-driven topic extraction from online reviews to develop a service quality assessment framework. The framework is based on topic and sentiment classification methods. We conducted an empirical analysis to assess the validity of the framework. Specifically, we examined if patients' sentiments regarding our extracted dimensions affect demand (number of consultation requests) due to quality signals reflected in these dimensions.

Results: We develop a 5-dimensional health care service quality framework (HSQ-5D model). In the empirical study, patient demand is affected by these dimensions, including expertise (coefficient=1.12; P<.001), service delivery process (coefficient=5.60; P<.001), attitude (coefficient=0.82; P<.001), empathy (coefficient=2.65; P<.001), and outcome (coefficient=0.26; P<.001; through patients' perceived quality from reviews). The 5 dimensions can explain 85.52% of the variance in patient demand, while all information from online reviews can explain 85.67%. The results show the validity and the potential practical value of the proposed HSQ-5D model.

Conclusions: This study explores how online reviews can be used to evaluate health care services, offering significant implications for health care management. Theoretically, we extend existing service quality frameworks by integrating text-mining analysis of online reviews, thereby enhancing the understanding of service quality assessment in the digital health context. Practically, the framework can allow health care platforms to identify and reveal doctors' service quality to reduce patients' information asymmetry and strengthen patient-provider relationships, ultimately contributing to a more effective and patient-centered health care system.

基于在线评论的卫生保健服务质量评估框架的开发:文本挖掘研究。
背景:随着在线医疗平台的发展,患者点评已成为评估医疗服务质量的重要来源。然而,在文本审查的质量维度的关键方面仍然很大程度上未被探索。目的:利用在线评价数据,建立医疗服务质量综合评价框架。这样一个框架将支持大型服务提供商,如在线平台,有效地评估许多医生的质量。方法:采用文本挖掘方法,从在线评论中提取理论驱动的主题,开发服务质量评估框架。该框架基于主题和情感分类方法。我们进行了实证分析来评估该框架的有效性。具体来说,我们检查了由于这些维度中反映的质量信号,患者对我们提取的维度的看法是否会影响需求(咨询请求数量)。结果:构建了一个五维卫生保健服务质量框架(HSQ-5D模型)。在实证研究中,患者需求受到以下维度的影响:专业知识(系数=1.12;结论:本研究探讨了如何使用在线评论来评估卫生保健服务,为卫生保健管理提供重要启示。从理论上讲,我们通过整合在线评论的文本挖掘分析来扩展现有的服务质量框架,从而增强对数字健康背景下服务质量评估的理解。实际上,该框架可以让医疗保健平台识别和揭示医生的服务质量,以减少患者的信息不对称,加强医患关系,最终有助于建立一个更有效、以患者为中心的医疗保健系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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