Objective: Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. Methods: A simple negation algorithm was applied to ten types of clinical reports (n=42,160) dictated during July2000. We counted how often each of 66 negation phrases was used to mark a clinical observation as absent. Physicians read a random sample of 400 sentences, and precision was calculated for the negation phrases. We measured what proportion of clinical observations were marked as absent. Results: The negation algorithm was triggered by sixty negation phrases with just seven ofthe phrases accounting for 90% of the negations. The negation phrases received an overall precision of 97%, with “not” earning the lowest precision of 63%. Between 39% and 83% of all clinical observations were identified as absent by the negation algorithm, depending on the type of report analyzed. The most frequently used clinical observations were negated the majority ofthe time. Conclusion: Because clinical observations in textual patient records are frequently negated, identifying accurate negation phrases is important to any system processing these reports.