ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents
Researchers have introduced ClinEnv, a novel interactive benchmark designed to evaluate large language models (LLMs) in simulated clinical settings. This environment presents LLMs with real inpatient admissions, requiring them to act as attending physicians who must gather information sequentially and make irreversible decisions under uncertainty. Unlike static benchmarks, ClinEnv allows models to actively query specialized agents at each stage, enabling a more realistic assessment of both decision-making and information-gathering processes. Initial evaluations across seven models revealed significant gaps, with the strongest performer achieving only a 0.31 decision F1 score, highlighting a critical need for improvement in clinical reasoning and management. AI
IMPACT This benchmark could accelerate the development of more capable AI agents for complex, sequential decision-making tasks in specialized domains like healthcare.