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.
Ballerio, Stefano. "Automatic Analysis of Electronic Discharge Letters as a Means to Evaluate the Continuity of Information and of Patient Care." In Rethinking Electronic Publishing: Innovation in Communication Paradigms and Technologies - Proceedings of the 13th International Conference on Electronic Publishing, 607-612. ELPUB. Milano, Italy, 2009.