What Do Patients Care About? Mining Fine-grained Patient Concerns from Online Physician Reviews Through Computer-Assisted Multi-level Qualitative Analysis.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: Jan 25, 2021

Abstract

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.

Authors

  • Lu He
    University of California, Irvine, Irvine, CA, USA.
  • Changyang He
    Hong Kong University of Science and Technology, Hong Kong, China.
  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Zhaoxian Hu
    University of California, Irvine, Irvine, CA, USA.
  • Kai Zheng
    University of California, Irvine, Irvine, CA, USA.
  • Yunan Chen
    University of California, Irvine, Irvine, CA, USA.

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