Medical histories provide a rich resource for diagnoses and treatment. Similarly, consumers? blog postings on health-related topics offer unique data for medical researchers, practitioners, and pharmacologists. Nevertheless, speech and text analytic programs for mining recordings of patients? histories or consumers? blogs are compromised by the ambiguities, repetitions and ellipses in natural speech, which can be more pronounced when the speaker or blogger is discussing a medical problem. Conventional systems or programs limited to a set of key words and phrases cannot process speech as it actually occurs; if a speaker or blogger fails to use the word(s) found in the speech application?s vocabulary, a poor statistical word match (or no match) is given. This paper shows how Sequence Package Analysis is informed by algorithms that can work with, rather than be hindered by, less than perfect natural speech for intelligent mining of doctor-patient recordings and blogs.