On the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management

The paper proposes a framework to evaluate the reliability of large language model (LLM) agents when they generate stakeholder preferences for multi‑criteria decision‑making in healthcare operations. It examines both explicit preferences—agents rating the importance of patient waiting time, provider workload, and operating cost in an outpatient scheduling scenario—and implicit preferences—agents’ pairwise choices between emergency department configurations. Experiments show that explicit preferences vary by role and operating conditions, but deliberation can homogenize them, while implicit preferences are heavily influenced by presentation order and only modestly improved by contextual models. Robust weighted‑sum optimization models that incorporate preference uncertainty often yield different schedules than those based on average weights, underscoring the need for auditing LLM‑generated preferences before using them in healthcare decision support.

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• llms
• agents