At the time of data acquisition in a longitudinal study a decision needs to be made whether or not the latest measurement of the primary outcome is a potential outlier. If the data point does not fit with the subject's prior data, the patient can be immediately remeasured before he/she leaves the office. From the third visit onwards, a least squares approach can be used to generate prediction intervals for the value of the response at that visit. We propose a Bayesian method for calculating a prediction interval that can incorporate external information about the process that can be used beginning at the first visit. Both the least squares and Bayesian approaches will be used to prospectively clean longitudinal data. An example using longitudinally measured bone density measurements in the elderly will be discussed. In addition, simulation studies will be described which show that both cleaning methods are better than doing nothing and that the Bayesian approach outperforms the least squares method.