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Medicine

CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation

Ruihui Hou, Ziyue Huai, Chennuo Zhang, Ziyan Liu, Siran Zhao, Yao Yu, Jie Zhai, Tong Ruan

Featured June 7, 2026

Original Paper

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Simply

CAREAgent helps doctors by turning medical decisions into precise, step-by-step treatment plans using structured reasoning and tool-integrated learning, ensuring that every medication and test order is safe, accurate, and ready for execution.

In depth
The paper introduces CAREAgent, a framework that bridges the gap between clinical decision-making and executable orders by utilizing a two-stage agentic reasoning data construction method. By combining supervised fine-tuning with agentic reinforcement learning using multi-dimensional rewards, the model achieves superior performance in generating structured, clinically valid, and verifiable medical orders.

Key Takeaways

  • 1
    The authors introduce a two-stage agentic reasoning data construction pipeline that generates high-quality, verifiable clinical trajectories without manual annotation.
  • 2
    The framework employs a two-stage post-training strategy, combining supervised fine-tuning with reinforcement learning to optimize for format, reasoning, and order correctness.
  • 3
    Extensive experiments demonstrate that CAREAgent significantly outperforms existing single-agent and multi-agent baselines on clinical benchmarks, particularly in recall and safety metrics.

Conceptual Flow

HIGH LEVEL
1
Methodology

The system learns by first practicing with expert-like examples and then refining its skills through a reward system that checks if its medical orders are correct and safe.

Patient Records
Clinical Tools
Process through reasoning stages
Structured Treatment Plan
2
Results

The new method produces more accurate and complete medical orders compared to older systems, leading to safer patient care.

Old Methods

New Method

Compare performance scores

Higher Accuracy