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Using large language model to guide patients to create efficient and comprehensive clinical care message.

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Large Language Models (LLMs) can generate clarifying follow-up questions for patient messages, improving communication efficiency. A fine-tuned LLM, CLAIR, showed comparable clarity and conciseness to human providers, with superior utility.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Communication Systems
  • Natural Language Processing

Background:

  • Patient-provider communication often involves delays due to incomplete initial messages.
  • Ensuring healthcare providers receive adequate information is crucial for safe and accurate medical advice.

Purpose of the Study:

  • To assess the feasibility of using Large Language Models (LLMs) to generate patient-facing follow-up questions.
  • To improve the completeness of information in patient messages before they are sent to providers.
  • To reduce communication friction and delays in healthcare interactions.

Main Methods:

  • Collected patient messages from Vanderbilt University Medical Center (Jan 2022 - Mar 2023).
  • Developed and tested three LLMs: CLAIR (fine-tuned), GPT4 (simple prompt), GPT4 (complex prompt).
  • Physicians evaluated LLM-generated questions against human-generated ones for clarity, completeness, conciseness, and utility.

Main Results:

  • CLAIR model demonstrated superior performance in five out of seven scenarios.
  • CLAIR questions showed similar clarity and conciseness to human-generated questions.
  • CLAIR offered higher utility than both human providers and GPT4, though with lower completeness than GPT4.

Conclusions:

  • LLMs are feasible tools for generating effective patient message clarifications.
  • LLM-generated follow-up questions can favorably compare to those from healthcare providers.
  • This technology has the potential to streamline patient-provider communication.