Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those