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Updated: May 5, 2026

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Unlikely Storyteller: Leveraging Narrative-Based Communication in LLM-Generated Medical Advice.

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Summary

Narrative prompting improved AI communication in online consultations by enhancing empathy and personalization. This approach offers a scalable strategy for better patient-centered interactions in digital health settings.

Keywords:
algorithm aversionlarge language models (LLMs)narrative medicinepatient-centered communicationprompt engineering

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Clinical Communication

Background:

  • Patient-centered communication is often limited in time-constrained clinical settings.
  • AI-generated medical advice may suffer from algorithm aversion due to a lack of humanistic elements.
  • Improving communication in asynchronous text-based consultations is crucial.

Purpose of the Study:

  • To investigate if a narrative-based prompt can enhance patient-facing communication features in a large language model (LLM).
  • To compare LLM responses with and without narrative prompting against human clinician responses.
  • To evaluate communication quality using predefined metrics for storytelling, empathy, personalization, and clarity.

Main Methods:

  • A comparative evaluation of 1000 de-identified Chinese online consultations (MedDialog-CN).
  • Three conditions: human clinician response, zero-shot LLM response, and narrative-prompted LLM response.
  • Responses were annotated for communication dimensions (Storytelling, Empathy, Personalization, Clarity) by expert adjudicators.

Main Results:

  • Narrative prompting standardized and improved coded patient-facing communication features.
  • The prompted LLM demonstrated superior narrative device use, empathic support, and contextual tailoring.
  • Prompted responses showed higher rates of emotion-symptom linkage and patient-centered narrative frameworks.

Conclusions:

  • Narrative prompting is a potentially scalable strategy for enhancing patient-facing communication in asynchronous online consultations.
  • Careful calibration is needed to ensure humanistic cues are delivered safely and credibly.
  • Further research should focus on optimizing the delivery of humanistic elements in AI-generated medical advice.