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  1. Home
  3. Evaluating Large Language Models For Specialist Referral Triage In Primary Care: A Quantitative Study Using Otolaryngology Scenarios.
  1. Home
  3. Evaluating Large Language Models For Specialist Referral Triage In Primary Care: A Quantitative Study Using Otolaryngology Scenarios.

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Evaluating large language models for specialist referral triage in primary care: a quantitative study using

Sholem Hack1, Rebecca Attal1, David Yogev2

  • 1City St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center, Ramat Gan 52621, Israel.

Family Practice
|November 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in aiding primary care referral decisions. Structured input is crucial for AI tools to ensure safe and reliable specialist triage, especially for ear, nose, and throat conditions.

Keywords:
artificial intelligenceclinical decision supportfamily medicinelarge language modelsprimary carereferral triage

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Primary care providers face challenges in specialist referral triage, leading to delays and system strain.
  • Artificial intelligence (AI) presents opportunities to improve referral decision-making in primary care.
  • Timely and effective specialist referrals are critical for patient outcomes.

Purpose of the Study:

  • To evaluate the performance and reliability of two AI models as referral decision support tools.
  • To assess AI model responses to simulated primary care scenarios involving ear, nose, and throat (ENT) conditions.
  • To compare AI model performance using structured clinical versus informal patient-language prompts.

Main Methods:

  • Sixteen clinical vignettes (common/high-stakes ENT presentations) were used.
  • AI models received input in structured clinical and informal patient-language formats.
  • Responses were assessed by otolaryngologists and lay reviewers using a standardized rubric.

    • Main Results:

    • Both AI models provided safe and appropriate referral recommendations with structured input.
    • No significant performance differences were found between the two AI models.
    • One AI model's performance decreased with informal language prompts; reviewer agreement was high.

    Conclusions:

    • AI decision support tools can assist in primary care specialist referral triage.
    • Structured, clear input is essential for maximizing AI safety and reliability in referrals.
    • AI holds potential for enhancing the efficiency and accuracy of healthcare referrals.