University of California, Irvine, Irvine, CA, USA.
Hong Kong University of Science and Technology, Hong Kong, China.
AMIA Annu Symp Proc. 2021 Jan 25;2020:544-553. eCollection 2020.
Online physician review (OPR) websites have been increasingly used by healthcare consumers to make informed decisions in selecting healthcare providers. However, consumer-generated online reviews are often unstructured and contain plural topics with varying degrees of granularity, making it challenging to analyze using conventional topic modeling techniques. In this paper, we designed a novel natural language processing pipeline incorporating qualitative coding and supervised and unsupervised machine learning. Using this method, we were able to identify not only coarse-grained topics (e.g., relationship, clinic management), but also fine-grained details such as diagnosis, timing and access, and financial concerns. We discuss how healthcare providers could improve their ratings based on consumer feedback. We also reflect on the inherent challenges of analyzing user-generated online data, and how our novel pipeline may inform future work on mining consumer-generated online data.
在线医生评论（OPR）网站越来越多地被医疗保健消费者用于做出明智的决策，选择医疗服务提供者。然而，消费者生成的在线评论往往是非结构化的，并且包含多个具有不同粒度的主题，这使得使用传统的主题建模技术进行分析变得具有挑战性。在本文中，我们设计了一种新颖的自然语言处理管道，结合了定性编码以及监督和无监督机器学习。使用这种方法，我们不仅能够识别出粗粒度的主题（例如，关系、诊所管理），还能够识别出诊断、时间和访问以及财务问题等细粒度的细节。我们讨论了医疗服务提供者如何根据消费者的反馈来提高他们的评分。我们还反思了分析用户生成的在线数据所面临的固有挑战，以及我们的新管道如何为挖掘消费者生成的在线数据的未来工作提供信息。
