With the remarkable performance of large language models (LLMs) in medicine, particularly their ability to support clinical decision-making in medical dialogues, a key limitation remains: the static reasoning patterns derived from human expert experience are often inadequate for the dynamic and diverse nature of real-world multi-turn conversations. While recent large reasoning models (such as R1) enable deeper and more complex thought processes to address such challenges, they also introduce significant redundancy. Meanwhile, recent studies on reusing atomic thoughts demonstrate a practical pathway toward dynamic and precise reasoning in general domains. In this paper, we investigate the role of atomic thought-based experience in medical dialogue tasks. First, we collect human expert clinical experience. Then, we propose a novel distillation framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. Based on this framework, we construct training data from ReMeDi and fine-tune student models, which demonstrate enhanced performance in both static and interactive medical dialogue scenarios. Furthermore, we examine the impact of experience across various models, datasets, and scenarios. Crucially, transferring this experience empowers weaker models to generate high-quality reasoning data, matching the annotation capabilities of stronger LLMs while significantly reducing costs. The code is available in this repository https://github.com/VioletAmethystLunar/Atomic-Thoughts-Medical-Dialogue.