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

IMPORTANCE

Optimizing order sets is vital to enhance clinical decision support and improve patient care. Manual review is resource intensive and cannot timely identify potential improvements in order sets.

OBJECTIVE

To develop and evaluate the utility of a large language model (LLM)-powered multiagent system in optimizing order sets.

DESIGN, SETTING, AND PARTICIPANTS: A multiagent system was developed and evaluated between January 1, 2024, and December 31, 2024, which comprised agents for content critique, dynamic search, knowledge retrieval, medication verification, and suggestion summarization. A filter was developed to align suggestion usefulness scores with expert preferences. Experiment 1 evaluated 735 generated suggestions from a multiagent system developed for optimizing order sets, which were assessed by 3 physicians for 9 order sets and by 1 physician for 62 order sets. Experiment 2 implemented an LLM-as-a-judge approach to align generated suggestions with expert ratings and developed a filter to further refine the system's performance. The study was performed at Vanderbilt University Medical Center. A total of 735 suggestions for 71 order sets at VUMC were evaluated by 3 physicians.

MAIN OUTCOMES AND MEASURES

The ratings of accuracy, usefulness, feasibility, and impact; interrater agreement; and alignment against historical ordering data.

RESULTS

In evaluation 1 of experiment 1, the median values for the number of suggestions scoring 4 or higher at the order set level were 5 (IQR, 5-6) for the metrics of accuracy, 2 (IQR, 1-4) for usefulness, 1 (IQR, 0-3) for feasibility, and 1 (IQR, 0-2) for impact. Of 96 suggestions, 44 (46%; 95% CI, 36%-56%) aligned with historical ordering patterns. In evaluation 2 of experiment 1, 639 suggestions were generated for 62 order sets; 52 order sets had at least 1 useful suggestion, with a median of 2 (IQR, 1-3) useful suggestions. Overall, 122 suggestions (19%; 95% CI, 16%-22%) were rated as useful. After expert alignment, Cohen κ improved from 0.06 to 0.41. Filtering using the aligned scores reduced total suggestions by 29% while retaining 92% of useful suggestions.

CONCLUSIONS AND RELEVANCE

In this cohort study of an LLM-powered multiagent system for optimizing order sets, leveraging LLMs and multiagent systems provided a scalable approach. Alignment with a small set of expert ratings significantly enhanced the LLM evaluation. Future research could refine reasoning capabilities and integrate useful suggestions into electronic health records, while engaging end-users as artificial intelligence-supported reviewers.