Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines

Abstract

Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, approaches for incorporating CPGs into LLMs are not well studied. In this study, we develop three distinct methods for incorporating CPGs into LLMs: Binary Decision Tree (BDT), Program-Aided Graph Construction (PAGC), and Chain-of-Thought-Few-Shot Prompting (CoT-FSP), and focus on CDS for COVID-19 outpatient treatment as the case study. Zero-Shot Prompting (ZSP) is our baseline method. To evaluate the effectiveness of the proposed methods, we create a set of synthetic patient descriptions and conduct both automatic and human evaluation of the responses generated by four LLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. All four LLMs exhibit improved performance when enhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP and PAGC in automatic evaluation. All of the proposed methods demonstrate high performance in human evaluation. LLMs enhanced with CPGs outperform plain LLMs with ZSP in providing accurate recommendations for COVID-19 outpatient treatment, highlighting the potential for broader applications beyond the case study.

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