  Most medical dialogue systems assume that patients have clear goals (medicine
querying, surgical operation querying, etc.) before medical consultation.
However, in many real scenarios, due to the lack of medical knowledge, it is
usually difficult for patients to determine clear goals with all necessary
slots. In this paper, we identify this challenge as how to construct medical
consultation dialogue systems to help patients clarify their goals. To mitigate
this challenge, we propose a novel task and create a human-to-human mixed-type
medical consultation dialogue corpus, termed MidMed, covering five dialogue
types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded
dialogue, QA, and chitchat. MidMed covers four departments
(otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,175
dialogues. Furthermore, we build baselines on MidMed and propose an
instruction-guiding medical dialogue generation framework, termed InsMed, to
address this task. Experimental results show the effectiveness of InsMed.
