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Optimizing Order Sets With a Large Language Model-Powered Multiagent System.

Siru Liu1,2, Sean S Huang1,3, Allison B McCoy1

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.

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Summary
This summary is machine-generated.

A large language model (LLM)-powered multiagent system effectively optimizes clinical order sets. This AI approach enhances decision support and patient care by providing scalable, expert-aligned suggestions for order set improvement.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Informatics
  • Health Systems Engineering

Background:

  • Optimizing clinical order sets is crucial for enhancing decision support and patient care.
  • Manual review of order sets is time-consuming and inefficient for identifying improvements.
  • Large language models (LLMs) offer potential for automating and improving order set optimization.

Purpose of the Study:

  • To develop and evaluate a large language model (LLM)-powered multiagent system for optimizing clinical order sets.
  • To assess the utility and effectiveness of AI-driven suggestions in improving order set accuracy, usefulness, feasibility, and impact.

Main Methods:

  • Developed a multiagent system with specialized agents for critique, search, knowledge retrieval, medication verification, and summarization.
  • Implemented an LLM-as-a-judge approach and a filtering mechanism to align AI suggestions with expert preferences and clinical data.
  • Evaluated 735 generated suggestions across 71 order sets by physicians at Vanderbilt University Medical Center.

Main Results:

  • The multiagent system generated numerous suggestions, with a median of 2 useful suggestions per order set after refinement.
  • Expert alignment significantly improved the agreement of AI suggestions, increasing Cohen's κ from 0.06 to 0.41.
  • Filtering reduced the total number of suggestions by 29% while preserving 92% of those deemed useful.

Conclusions:

  • LLM-powered multiagent systems offer a scalable solution for optimizing clinical order sets.
  • Aligning AI-generated suggestions with expert ratings is critical for enhancing evaluation accuracy.
  • Future work should focus on refining AI reasoning and integrating these tools into electronic health records for real-time clinical support.