Suppr超能文献

基于在线评论的医疗服务质量评估框架的构建:文本挖掘研究

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

作者信息

Zhang Xue, Sun Jianshan, Li Xin, Liu Yezheng, Li Chenwei

机构信息

Fuyang Normal University, Fuyang, China.

Hefei University of Technology, 193 Tunxi Road, Hefei, 230009, China, 86 18956561661.

出版信息

J Med Internet Res. 2025 Jul 9;27:e66141. doi: 10.2196/66141.

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.

摘要

背景

随着在线医疗平台的发展,患者评价已成为评估医疗服务质量的重要来源。然而,文本评价中质量维度的关键方面在很大程度上仍未得到探索。

目的

本研究旨在通过利用在线评价数据建立一个全面的医疗服务质量评估框架。这样一个框架将支持大型服务提供商,如在线平台,有效地评估众多医生的质量。

方法

我们采用文本挖掘方法,从在线评价中进行理论驱动的主题提取,以开发一个服务质量评估框架。该框架基于主题和情感分类方法。我们进行了实证分析以评估该框架的有效性。具体而言,我们研究了患者对我们提取维度的情感是否会由于这些维度中反映的质量信号而影响需求(咨询请求数量)。

结果

我们开发了一个5维医疗服务质量框架(HSQ - 5D模型)。在实证研究中,患者需求受这些维度影响,包括专业知识(系数 = 1.12;P <.001)、服务提供过程(系数 = 5.60;P <.001)、态度(系数 = 0.82;P <.001)、同理心(系数 = 2.65;P <.001)和结果(系数 = 0.26;P <.001;通过患者从评价中感知的质量)。这5个维度可以解释患者需求方差的85.52%,而来自在线评价的所有信息可以解释85.67%。结果表明了所提出的HSQ - 5D模型的有效性和潜在实用价值。

结论

本研究探索了如何利用在线评价来评估医疗服务,对医疗管理具有重要意义。从理论上讲,我们通过整合在线评价的文本挖掘分析扩展了现有的服务质量框架,从而增强了对数字健康背景下服务质量评估的理解。在实践中,该框架可以使医疗平台识别并揭示医生的服务质量,以减少患者的信息不对称并加强医患关系,最终有助于建立一个更有效且以患者为中心的医疗系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/efcd/12266612/c70875c8bb1a/jmir-v27-e66141-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验