Zhou Sijia, Xie Kaihui, Zhang Xinzhe
Southeast University, Nanjing, China.
University of California Berkeley, Berkeley, CA, USA.
Digit Health. 2025 Aug 29;11:20552076251353320. doi: 10.1177/20552076251353320. eCollection 2025 Jan-Dec.
This study aims to prioritize service attributes related to doctors' online performance based on patients' reviews on online healthcare platforms (OHPs).
We propose a three-stage framework based on uncertainty reduction theory. First, perceived service attributes are extracted from review texts through aspect-based sentiment analysis using deep learning models. Second, the impact of these attributes on customer satisfaction and subsequent consultation demand is prioritized using extreme gradient boosting and an econometric model, respectively. Third, the service strengths and weaknesses of individual doctors are evaluated, to provide recommendations for targeted improvements through importance-performance analysis.
A dataset of 445,435 reviews involving 49,024 doctors from an OHP were analyzed based on our framework. We find that attributes reducing cognitive uncertainty such as attitude are more influential than those addressing behavioral uncertainty like professional skill or fit uncertainty like overall experience.
We built a three-stage framework for mining the perceived service attributes and ranked their priority, which is conducive to doctors' use of patient feedback on OHPs to adjust their service focus and develop strategies to improve service quality.
本研究旨在根据患者在在线医疗平台（OHP）上的评价，对与医生在线表现相关的服务属性进行优先级排序。
我们提出了一个基于不确定性降低理论的三阶段框架。首先，通过使用深度学习模型的基于方面的情感分析，从评论文本中提取感知服务属性。其次，分别使用极端梯度提升和计量经济模型，对这些属性对客户满意度和后续咨询需求的影响进行优先级排序。第三，评估个体医生的服务优势和劣势，通过重要性-绩效分析提供有针对性改进的建议。
基于我们的框架，对来自一个OHP的涉及49024名医生的445435条评价数据集进行了分析。我们发现，减少认知不确定性的属性，如态度，比解决行为不确定性的属性（如专业技能）或解决契合不确定性的属性（如整体体验）更具影响力。
我们构建了一个三阶段框架来挖掘感知服务属性并对其优先级进行排序，这有助于医生利用OHP上的患者反馈来调整服务重点并制定提高服务质量的策略。
