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Unsupervised Large Language Models to Identify Topics in Cancer Center Patient Portal Messages.

Ji Hyun Chang1,2, Amir Ashraf-Ganjouei1, Isabel Friesner1

  • 1Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.

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Summary
This summary is machine-generated.

Patient portal messages have surged, especially scheduling inquiries, increasing physician workload. New strategies and AI are needed to manage volume and prevent burnout.

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Area of Science:

  • Health Informatics
  • Oncology Communication
  • Natural Language Processing

Background:

  • Patient portals enhance communication but increase provider message volume.
  • High message volume contributes to physician burnout.

Purpose of the Study:

  • To analyze the trends and topics of patient-generated portal messages.
  • To identify the impact of message volume on healthcare providers.

Main Methods:

  • Utilized BERTopic (a natural language processing technique) and GPT-4 for topic modeling and categorization of over 2.2 million patient portal messages.
  • Employed Uniform Manifold Approximation and Projection for visualization and Student's t-test for volume analysis.
  • Messages from a single cancer center (2011-2023) were analyzed.

Main Results:

  • Message volume increased significantly from 2,071 monthly in 2012 to 43,430 in 2022.
  • A notable increase in message volume was observed post-COVID-19 pandemic.
  • Scheduling-related messages were most frequent, followed by symptoms and medication inquiries, with no decrease despite self-scheduling system changes.

Conclusions:

  • The dramatic rise in patient portal messages necessitates improved communication strategies to alleviate provider burden.
  • Findings support the development of AI-driven triage systems to manage message volume and combat physician burnout.
  • Addressing message overload is crucial for maintaining quality patient care and provider well-being.