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Topic Modeling for International Patients' Consultations Using Natural Language Processing.

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Analyzing 1452 consultations using natural language processing revealed six key topics. These insights into medical opinions and non-medical services are vital for improving hospital marketing and digital solutions.

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Marketing Analytics

Background:

  • Effective patient consultation is crucial for healthcare providers.
  • Understanding consultation content can inform service improvement and marketing strategies.
  • Digital platforms are increasingly important for patient engagement.

Purpose of the Study:

  • To identify major topics within patient consultation data.
  • To categorize consultation themes into medical and non-medical aspects.
  • To provide data-driven recommendations for marketing and digital solutions.

Main Methods:

  • Applied natural language processing (NLP) techniques.
  • Utilized keyword extraction algorithms: TF, TF-IDF, TextRank, Yake, and KeyBERT.
  • Analyzed 1452 consultation records from website and email sources.

Main Results:

  • Identified six distinct topics from the consultation data.
  • Topics were categorized into 'Medical opinion' (related to hospital characteristics) and 'Non-medical service guidance'.
  • Analysis revealed specific areas for potential marketing and digital solution development.

Conclusions:

  • The identified topics provide valuable insights into patient needs and perceptions.
  • A strategic marketing plan is necessary to address these findings.
  • Development of a digital solution is recommended for optimizing consultation effectiveness.