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Background Large Language Models (LLMs), enhanced with Clinical PracticeGuidelines (CPGs), can significantly improve Clinical Decision Support (CDS).However, methods for incorporating CPGs into LLMs are not well studied. MethodsWe develop three distinct methods for incorporating CPGs into LLMs: BinaryDecision Tree (BDT), Program-Aided Graph Construction (PAGC), andChain-of-Thought-Few-Shot Prompting (CoT-FSP). To evaluate the effectiveness ofthe proposed methods, we create a set of synthetic patient descriptions andconduct both automatic and human evaluation of the responses generated by fourLLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. Zero-Shot Prompting (ZSP) wasused as the baseline method. We focus on CDS for COVID-19 outpatient treatmentas the case study. Results All four LLMs exhibit improved performance whenenhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSPand PAGC in automatic evaluation. All of the proposed methods demonstrated highperformance in human evaluation. Conclusion LLMs enhanced with CPGs demonstratesuperior performance, as compared to plain LLMs with ZSP, in providing accuraterecommendations for COVID-19 outpatient treatment, which also highlights thepotential for broader applications beyond the case study.

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