TY - JOUR
T1 - Developing a Framework for Online Review-Based Health Care Service Quality Assessment
T2 - Text-Mining Study
AU - Zhang, Xue
AU - Sun, Jianshan
AU - Li, Xin
AU - Liu, Yezheng
AU - Li, Chenwei
N1 - Publisher Copyright:
© Xue Zhang, Jianshan Sun, Xin Li, Yezheng Liu, Chenwei Li.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - health care service
KW - online reviews
KW - service quality
KW - SERVQUAL
KW - text mining
UR - https://www.scopus.com/pages/publications/105010528586
M3 - Article
C2 - 40633095
AN - SCOPUS:105010528586
SN - 1438-8871
VL - 27
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
M1 - e66141
ER -