Jin Xiaogang, Yuan Youwei, Chang Chaoqi, Wu Xianqing, Tan Xu, Liu Zhengqing
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China.
Digit Health. 2025 May 8;11:20552076251341163. doi: 10.1177/20552076251341163. eCollection 2025 Jan-Dec.
Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribution of healthcare resources. The purpose of this study is to identify key factors affecting the service quality of telemedicine platforms in China, with the dual objectives of advancing patient wellbeing and informing evidence-based service innovations for industry stakeholders.
To quantitatively assess the impact of these key factors on health and wellbeing from the perspective of healthcare consumers, a total of 25,499 valid online reviews were collected from telemedicine platforms. To establish a service quality evaluation framework, this study proposes a novel approach that combines the Servqual quality assessment model with a CNN-BiLSTM deep learning model enhanced by an attention mechanism.
Analysis of the full sample shows that healthcare consumers are most concerned about the quality of services provided by telemedicine platforms, with the most important being the professional competence of doctors, a critical factor for promoting consumer health and wellbeing. The proposed hybrid deep learning approach demonstrates superior performance in sentiment classification accuracy, outperforming conventional methods by 11.11 percentage points. This methodological innovation enables more precise identification of consumer sentiment patterns across service dimensions.
The novel quality assessment framework introduced here provides actionable insights for advancing telemedicine platforms, driving progress toward precision healthcare and consumer-centric wellbeing. Furthermore, it enables healthcare consumers to select telemedicine services aligned with their personalized needs.
远程医疗平台在新冠疫情期间发挥了关键作用，缓解了与医疗资源短缺和分配不均相关的问题。本研究的目的是确定影响中国远程医疗平台服务质量的关键因素，以实现提升患者福祉以及为行业利益相关者提供循证服务创新的双重目标。
为了从医疗消费者的角度定量评估这些关键因素对健康和福祉的影响，共从远程医疗平台收集了25499条有效的在线评论。为建立服务质量评估框架，本研究提出了一种新颖的方法，将Servqual质量评估模型与通过注意力机制增强的CNN-BiLSTM深度学习模型相结合。
对全样本的分析表明，医疗消费者最关心远程医疗平台提供的服务质量，其中最重要的是医生的专业能力，这是促进消费者健康和福祉的关键因素。所提出的混合深度学习方法在情感分类准确率方面表现出卓越性能，比传统方法高出11.11个百分点。这种方法创新能够更精确地识别跨服务维度的消费者情感模式。
本文介绍的新颖质量评估框架为推进远程医疗平台提供了可操作的见解，推动精准医疗和以消费者为中心的福祉发展。此外，它使医疗消费者能够选择符合其个性化需求的远程医疗服务。
