Prompt engineering on leveraging large language models in generating response to InBasket messages.

ABSTRACT:

Objectives

Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.

Materials and methods

We designed a novel human-involved iterative processes to train and validate prompts to LLM in creating appropriate responses to PMARs. GPT-4 was used to generate response to the messages. We updated the prompts, and evaluated both clinician and patient acceptance of LLM-generated draft responses at each iteration, and tested the optimized prompt on independent validation data sets. The optimized prompt was implemented in the electronic health record production en

SUBMITTER: Yan S 

PROVIDER: S-EPMC11413421 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature