Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation

TL;DR

This study explores the use of a multi-agent system based on Large Language Models to improve the safety and effectiveness of therapy recommendations for patients with multiple chronic conditions, comparing it to single-agent and real-world benchmarks.

Contribution

It introduces a novel multi-agent framework simulating multidisciplinary decision-making and develops new clinical evaluation metrics for therapy recommendation systems.

Findings

Single-agent LLM performs as well as MDTs in recommendations.

Best models meet all clinical goals but are sometimes incomplete.

Some models suggest unnecessary medications causing conflicts.

Abstract

Therapy recommendation for chronic patients with multimorbidity is challenging due to risks of treatment conflicts. Existing decision support systems face scalability limitations. Inspired by the way in which general practitioners (GP) manage multimorbidity patients, occasionally convening multidisciplinary team (MDT) collaboration, this study investigated the feasibility and value of using a Large Language Model (LLM)-based multi-agent system (MAS) for safer therapy recommendations. We designed a single agent and a MAS framework simulating MDT decision-making by enabling discussion among LLM agents to resolve medical conflicts. The systems were evaluated on therapy planning tasks for multimorbidity patients using benchmark cases. We compared MAS performance with single-agent approaches and real-world benchmarks. An important contribution of our study is the definition of evaluation metrics that go beyond the technical precision and recall and allow the inspection of clinical goals met and medication burden of the proposed advices to a gold standard benchmark. Our results show that with current LLMs, a single agent GP performs as well as MDTs. The best-scoring models provide correct recommendations that address all clinical goals, yet the advices are incomplete. Some models also present unnecessary medications, resulting in unnecessary conflicts between medication and conditions or drug-drug interactions.