  Recent advances in Automatic Speech Recognition (ASR) have made it possible
to reliably produce automatic transcripts of clinician-patient conversations.
However, access to clinical datasets is heavily restricted due to patient
privacy, thus slowing down normal research practices. We detail the development
of a public access, high quality dataset comprising of57 mocked primary care
consultations, including audio recordings, their manual utterance-level
transcriptions, and the associated consultation notes. Our work illustrates how
the dataset can be used as a benchmark for conversational medical ASR as well
as consultation note generation from transcripts.
