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  3. Cpgprompt: Translating Clinical Guidelines Into Large Language Model-executable Decision Support.
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  3. Cpgprompt: Translating Clinical Guidelines Into Large Language Model-executable Decision Support.

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CPGPrompt: translating clinical guidelines into large language model-executable decision support.

Ruiqi Deng1, Geoffrey Martin2,3, Tony Wang4

  • 1Information Science (Health Tech), Cornell Tech, Cornell University, New York, NY 10044, United States.

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|February 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

CPGPrompt, an AI system, converts clinical guidelines into large language models for better patient care. It shows promise across domains but needs improvement for subjective assessments.

Keywords:
AIclinical decision supportclinical practice guidelinesdecision treeslarge language models

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Natural Language Processing

Background:

  • Integrating clinical practice guidelines (CPGs) into artificial intelligence (AI) is challenging due to limitations in existing methods.
  • Previous AI approaches like rule-based systems or black-box models lack interpretability and domain applicability.

Purpose of the Study:

  • To develop and validate CPGPrompt, an auto-prompting system that converts narrative CPGs into large language models (LLMs).
  • To address the limitations of current AI integration of CPGs for improved patient care.

Main Methods:

  • The CPGPrompt framework translates CPGs into structured decision trees.
  • A large language model (LLM) dynamically navigates these trees for patient case evaluation.
  • Synthetic vignettes across headache, lower back pain, and prostate cancer domains were used for testing.

    • Main Results:

    • CPGPrompt achieved strong performance in binary specialty referral classification (F1: 0.85-1.00) across all tested domains.
    • Multiclass pathway assignment performance varied by domain: headache (F1: 0.47), lower back pain (F1: 0.72), and prostate cancer (F1: 0.77).
    • Performance differences were linked to guideline structure, negation handling, temporal reasoning needs, and reliance on quantifiable data.

    Conclusions:

    • CPGPrompt demonstrates generalizability and high sensitivity for referral decisions, offering advantages over black-box AI.
    • The system's transparent framework aids in identifying failure modes.
    • Further improvements are needed for handling subjective clinical assessments to enhance clinical robustness.