Substantial medical data such as discharge summaries and operative reports are stored in textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. Terms that appear in these documents tend to appear in different contexts. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are those denied by the patient or subsequently "ruled out." Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. In this paper we examine the applicability of a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task of similar medical narratives and show its advantages. The new algorithm can be applied also to further context identification and information extraction tasks.