ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents

2026-06-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Health & Medical Research · Depth: Expert, quick

Summary

ClinEnv is an interactive benchmark designed to evaluate Large Language Models (LLMs) as attending physicians within real inpatient admissions, using a paradigm called Longitudinal Inpatient Simulation. Unlike static benchmarks, ClinEnv assesses LLMs' ability to gather heterogeneous information incrementally and make sequential, irreversible decisions under uncertainty. Each case is structured into ordered decision stages where models must actively query four specialized agents before committing to medications, procedures, and diagnoses. The benchmark scores both the quality of decisions, via deterministic ontology-grounded matching, and the process of information gathering. Initial evaluations across seven models show the strongest achieves only 0.31 decision F1, indicating a sharp decoupling between outcome and process quality. Difficulty concentrates in management decisions and later stages, with models recovering discharge diagnoses at 0.51 F1 but management actions at only 0.17 F1, often issuing redundant queries.

Key takeaway

For AI Scientists developing or evaluating LLMs for clinical decision support, you must recognize that current models significantly underperform in complex, multi-stage clinical scenarios. The 0.31 decision F1 score highlights a critical gap, particularly in management actions (0.17 F1) and efficient information gathering. Prioritize research into sequential decision-making, robust information acquisition, and generating reliable management actions to advance medical LLM capabilities beyond simple diagnostic recovery.

Key insights

ClinEnv reveals LLMs struggle with sequential, uncertain clinical decisions and information gathering in a multi-stage environment.

Principles

• Clinical practice involves sequential, irreversible decisions under uncertainty.
• Outcome-only evaluation misses critical information-acquisition gaps.
• Decision difficulty increases in later stages and for management actions.

Method

ClinEnv constructs real inpatient cases into ordered decision stages, requiring models to query four specialized agents before committing to actions, scoring both decisions and information gathering.

In practice

• Develop LLMs for sequential clinical decision-making.
• Focus on improving LLM performance in management actions.
• Optimize LLM information acquisition strategies.

Topics

• Clinical Decision Support
• Large Language Models
• Medical AI Benchmarking
• Multi-agent Systems
• Electronic Health Records
• Longitudinal Simulation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.