World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
[论文评述] World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
Summary
This study explored applying Reinforcement Learning (RL) to FHIR workflows, demonstrating the potential of 'World Feedback' for complex clinical decision-making. It identified structural limitations in existing models and proposed that a hybrid approach combining SFT and RL is optimal.
Details
This paper analyzes how Reinforcement Learning (RL) can be utilized for developing agents within FHIR-based clinical workflows. The researchers audited MedAgentBench v1/v2, identifying structural challenges such as the bias toward 'Inaction' due to 'Silent-finish' and the flattening of reward landscapes in tasks requiring precise knowledge like SNOMED codes. To address these issues, they constructed MAB-v3, an improved benchmark. This included balancing actions and no-actions, and introducing a hierarchical reward function (r = r_{terminal} + r_{action} + r_{penalty}) to encourage exploration. Experimental results showed that pure RL underperformed compared to SFT (Supervised Fine-Tuning). Specifically, for 'Format-knowledge' tasks requiring accurate clinical codes, mere environmental feedback was insufficient. The study concluded that injecting prior knowledge via SFT is essential. Therefore, the research proposes a hybrid approach—using SFT to inject necessary knowledge and RL to optimize conditional reasoning—as the most effective strategy for building clinical AI agents. This provides methodological guidance for designing future automated workflows.
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