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This study examines the effect of prompt engineering on the performance ofLarge Language Models (LLMs) in clinical note generation. We introduce anAutomatic Prompt Optimization (APO) framework to refine initial prompts andcompare the outputs of medical experts, non-medical experts, and APO-enhancedGPT3.5 and GPT4. Results highlight GPT4 APO's superior performance instandardizing prompt quality across clinical note sections. A human-in-the-loopapproach shows that experts maintain content quality post-APO, with apreference for their own modifications, suggesting the value of expertcustomization. We recommend a two-phase optimization process, leveragingAPO-GPT4 for consistency and expert input for personalization.

Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation