Despite strong performance in medical question answering, large language models remain limited in clinical cold-start settings, where little patient information is initially available and key evidence must be acquired through dialogue. This requires models to decide what to ask under a limited turn budget, balancing broad history taking with targeted clarification of high-risk cues. To address the aforementioned challenges, we propose **MedCIA**, a two-stage framework for improving clinical information acquisition. First, it reconstructs patient records into structured inquiry trajectories, enabling supervised learning of expert-guided information-seeking intents and the breadth-depth trade-off in clinical questioning. Second, it refines the policy with Adaptive Tree Policy Optimization (ATPO), an uncertainty-aware reinforcement learning procedure that allocates rollout computation to ambiguous dialogue states in a profile-anchored patient simulator. Furthermore, we curate a multi-turn dataset (MedIntake) for active clinical intake with progressive patient information disclosure and diverse inquiry strategies. Experiments on an EHR-derived benchmark show that **MedCIA** improves importance-weighted coverage, early acquisition, and cost control under controlled simulation.

Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM development, yield numerical scores that ignore the user experience. Therefore, our study shifts the focus from model-centered to human-centered evaluation in the context of AI-powered writing assistance applications. Our proposed metric, termed “Revision Distance,” utilizes LLMs to suggest revision edits that mimic the human writing process. It is determined by counting the revision edits generated by LLMs. Benefiting from the generated revision edit details, our metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. Our results show that for the easy-writing task, “Revision Distance” is consistent with established metrics (ROUGE, Bert-score, and GPT-score), but offers more insightful, detailed feedback and better distinguishes between texts. Moreover, in the context of challenging academic writing tasks, our metric still delivers reliable evaluations where other metrics tend to struggle. Furthermore, our metric also holds significant potential for scenarios lacking reference texts.