Aspect-based Sentiment Analysis of Radiology Patient Experience Surveys: A Cohort Study

Kurt Miller, Sunyang Fu, Kris Abah, Andrea Maraboto Escarria, Kevin Peterson, Hongfang Liu, Lacey Hart, Nelly Tan, Ming Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Institute of Healthcare Improvement considers experience of care as one of the Triple Aims. Radiology is among the highest volume departments in a health system. Despite being a cornerstone of quality, experience of care is poorly understood in Radiology. In radiology, Magnetic Resonance Imaging (MRI) is one of the most powerful and important medical imaging technologies for evaluate certain types of diseases. However, MRIs are among the most challenging imaging studies for patients due to long exams, loud noises during the exam, and the need to stay in a fixed position confined a narrow space for an extended time. Giving patients an opportunity to give feedback of their experiences after radiology visit can provide key patient-centered insights to elevate the experience of care for patients. The combination of simple, on-time electronic open-ended patient questionnaires and aspect-based sentiment analysis natural language processing (NLP) methods have enabled more accurate and prompt depictions of the patient experience, empowering operational and financial improvements in healthcare delivery. In this work, we create a labelled corpus of 146 post-MRI patient experience reviews by performing double-rated annotation, while iteratively developing annotation guidelines and resolving annotation inconsistencies. Sentiment-aspect pair segments were tagged by two expert annotators. Annotation guidelines, including the types of aspects and topics collected, were iteratively constructed to include the range and granularity of aspects for which patient had sentiment. Corpus analysis suggests waiting times, staff interactions and MRI discomfort were the primary sources of patient negative comment, but overall and staff-directed sentiment were positive. Subsequent work will extend the annotated corpus to a comprehensive gold standard dataset capable of distant supervision to supplement the corpus. Once sufficiently large, this corpus could be used to pretrain and train an aspect-based sentiment classification transformer model and deploy it as a real-time patient feedback engine to improve decision-making by health care providers and patient-centered care.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages90-96
Number of pages7
ISBN (Electronic)9781665468459
DOIs
StatePublished - 2022
Event10th IEEE International Conference on Healthcare Informatics, ICHI 2022 - Rochester, United States
Duration: Jun 11 2022Jun 14 2022

Publication series

NameProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022

Conference

Conference10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Country/TerritoryUnited States
CityRochester
Period6/11/226/14/22

Keywords

  • annotated corpus
  • aspect-based sentiment analysis
  • MRI
  • natural language processing
  • patient experience
  • radiology

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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