  Background Large Language Models (LLMs), enhanced with Clinical Practice
Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS).
However, methods for incorporating CPGs into LLMs are not well studied. Methods
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). 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. Zero-Shot Prompting (ZSP) was
used as the baseline method. We focus on CDS for COVID-19 outpatient treatment
as the case study. Results 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 demonstrated high
performance in human evaluation. Conclusion LLMs enhanced with CPGs demonstrate
superior performance, as compared to plain LLMs with ZSP, in providing accurate
recommendations for COVID-19 outpatient treatment, which also highlights the
potential for broader applications beyond the case study.
