Large Language Models have made impressive progress in the medical field. In medical dialogue scenarios, unlike traditional single-turn question-answering tasks, multi-turn doctor-patient dialogue tasks require AI doctors to interact with patients in multiple rounds, where the quality of each response impacts the overall model performance. In this paper, we propose PERT to re-explore values of multi-turn dialogue training data after the supervised fine-tuning phase by integrating a prefix learning strategy, further enhancing the response quality. Our preliminary results show that PERT achieves notable improvements on gynecological data, with an increase of up to 0.22 on a 5-point rating scale.