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Background: Information exists as unstructured medical text in healthcare databases. Such information is not routinely considered in safety surveillance but typically relies solely on structured (coded) data. Natural language processing (NLP) may allow the capture of concepts from unstructured data and thus enhance safety surveillance capability.Objectives: We sought to assess the added contribution of unstructured data extracted from medical text by NLP for detecting acute liver dysfunction (ALD) in patients with inflammatory bowel disease (IBD).Methods: Using a previously developed rule, we evaluated structured and unstructured NLP-extracted terms from a commercially available electronic medical record (EMR) system. The rule was intended to identify ALD diagnosis and timing of onset and was the result of three iterations of rule development using 150 ALD candidate cases. We evaluated the performance of the rule with or without NLP among all candidate cases and among 50 new cases with clinical adjudication.Results: NLP terms were necessary for the diagnosis of 9% of cases and for ruling out 3% of false-positive cases. Inclusion of NLP terms led to an identification of an additional  9% of ALD-onset dates, with consequent earlier recognition in 5%.Conclusions: NLP-derived terms in one large commercially available EMR system modestly improved the sensitivity and specificity in the identification of ALD and identified earlier onset. [ABSTRACT FROM AUTHOR]