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

Researchers introduce CAREAgent, an AI system designed to generate executable clinical orders by combining structured reasoning with tool integration. The model uses a two-stage training approach combining supervised fine-tuning and reinforcement learning, achieving 5.05% F1 score improvement over existing methods on clinical benchmarks.

CAREAgent addresses a critical gap in clinical AI development by moving beyond high-level medical decision-making to generate specific, actionable orders executable within real healthcare systems. This advancement matters because the translation from clinical reasoning to concrete actions has long been a weak point in medical AI deployment, where general medical knowledge alone cannot ensure orders meet safety standards and institutional requirements.

The research builds on growing recognition that healthcare AI requires domain-specific tooling and structured processes. Previous agent-based approaches struggled with fine-grained operational requirements, treating clinical order generation as a secondary output rather than a primary design consideration. CAREAgent's framework explicitly models tool usage patterns aligned with actual clinical workflows, creating a more realistic training environment than abstract benchmarks alone.

The two-stage training methodology—first establishing fundamental reasoning through supervised learning, then refining through multi-dimensional rewards—reflects lessons learned from recent AI development patterns. The filtering process for reasoning trajectories (format compliance, validity, clinical plausibility) reduces hallucinations and unsafe outputs, addressing safety concerns that have hampered medical AI adoption. Performance improvements across multiple baselines suggest meaningful progress, though gains diminish against stronger baselines, indicating incremental rather than breakthrough advancement.

For healthcare IT stakeholders and medical institutions, CAREAgent demonstrates that specialized agent design can improve clinical decision support system reliability. The research trajectory suggests ongoing incremental improvements in clinical AI executability over the next 12-18 months, with potential integration into electronic health record systems. Broader implications depend on whether external validation confirms the benchmark results and whether the approach generalizes across diverse clinical settings and order types.

• →CAREAgent generates executable clinical orders through structured reasoning and tool integration, addressing gaps in existing medical AI systems.
• →Two-stage training combining supervised fine-tuning and reinforcement learning with multi-dimensional rewards improves model performance and safety.
• →System achieves 5.05% F1 score improvement over single-agent baselines on unseen clinical benchmarks.
• →Framework explicitly filters reasoning trajectories for format compliance, order validity, and clinical plausibility to reduce errors.
• →Approach represents incremental progress in translating clinical reasoning into actionable healthcare system outputs.