Joint Commission International standard 3.2 on Access to Care and Continuity of Care states that discharge letters should contain information about follow-up instructions of doctors to patients. We developed a text mining system to analyze a collection of 413 discharge letters of heart failure patients and checked their compliance with standard 3.2. We built a domain-specific ontology and a thesaurus and mined the collection with CASOS AutoMap. After validation, the system sensitivity was 0.484; specificity was 0.834; positive predictive value was 0.555; negative predictive value was 0.790. Improving these results requires more powerful natural language processing tools, but text mining seems a promising way to evaluate the continuity of information and of care.