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Applying natural language processing and machine learning techniques to patient experience feedback: a systematic

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Natural Language Processing (NLP) and Machine Learning (ML) offer efficient ways to analyze patient feedback. These techniques, particularly supervised and unsupervised learning, can help healthcare organizations gain valuable insights from unstructured text data.

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Area of Science:

  • Health Informatics
  • Computational Linguistics
  • Data Science

Background:

  • Manual analysis of unstructured patient feedback is resource-intensive for healthcare organizations.
  • Patient feedback contains valuable insights for improving healthcare services.
  • Natural Language Processing (NLP) and Machine Learning (ML) offer automated solutions for analyzing large volumes of text data.

Purpose of the Study:

  • To systematically review the literature on NLP and ML applications for analyzing free-text patient experience data.
  • To identify common methodologies, data sources, and performance metrics used in NLP/ML for patient feedback analysis.
  • To assess the potential of NLP and ML in extracting meaningful insights from unstructured patient feedback.

Main Methods:

  • Systematic literature search of databases for articles published between January 2000 and December 2019.
  • Inclusion of studies focusing on NLP techniques for analyzing free-text patient feedback.
  • Narrative synthesis of data including study purpose, corpus, methodology, performance metrics, and quality indicators.

Main Results:

  • Nineteen articles were included in the review.
  • The majority of studies (80%) analyzed patient feedback from social media (unsolicited) and structured surveys (solicited).
  • Supervised learning (n=9) was frequently used, alongside unsupervised (n=6) and semi-supervised (n=3) approaches. Support Vector Machine and Naïve Bayes were top-performing ML classifiers.

Conclusions:

  • NLP and ML are valuable tools for processing unstructured free-text patient feedback.
  • The choice between supervised and unsupervised approaches depends on the data source.
  • Advancements in data analysis tools can empower healthcare organizations to derive insights from patient feedback data.