The development of Automatic Speech Recognition (ASR) technologies for healthcare applications is hindered by the limited availability of publicly accessible speech corpora that reflect both natural medical dialogues and the acoustic conditions typically found in clinical environments. In this study, we present the creation and characterization of MedDialogue-Audio, a new synthetic English-language corpus designed to address this gap. The dataset was derived from the MedDialog-EN transcription set and enriched through a multi-stage processing pipeline that involved text normalization with a large language model, speech synthesis, and the controlled addition of both white noise and hospital ambient sounds. We provide descriptive statistics for the corpus, which comprises more than 10,000 dialogues, as well as benchmarking results from leading ASR models. The experiments assess transcription performance across varying signal-to-noise ratios and establish baseline metrics to support future research in this field.