BACKGROUND: Researchers and policy makers are interested in identifying, implementing, and evaluating a national minimum data set for biosurveillance. However, work remains to be done to establish methods for measuring the value of such data. PURPOSE: The purpose of this article is to establish and evaluate a method for measuring the utility of biosurveillance data. METHOD: The authors derive an expected utility model in which the value of data may be determined by trading data relevance for time delay in receiving data. In a sample of 23 disease surveillance practitioners, the authors test if such tradeoffs are sensitive to the types of data elements involved (chief complaint v. emergency department [ED] log of visit) and proportional changes to the time horizon needed for receiving data (24 v. 48 h). In addition, they evaluate the logical error rate: the proportion of responses that scored less relevant data as having higher utility. RESULTS: Utilities of chief complaints were significantly higher than ED log of visit, F(1, 21)= 5.60, P < 0.05, suggesting the method is sensitive. Further utilities did not depend on time horizon used in the exercise, F(1, 21) = 0.00, P = ns. Of 92 time tradeoffs elicited, there were 5 logical errors (i.e., 5% logical error rate). CONCLUSIONS: In this article, the authors establish a time-tradeoff exercise for valuing biosurveillance data. Empirically, the method shows initial promise for evaluating a minimum data set for biosurveillance. Future applications of this approach may prove useful in disease surveillance planning and evaluation.