The initial recruitment timeline for a clinical trial is often determined using enrollment rates of past trials. Despite best efforts, challenges in identifying and recruiting the necessary patients in order to appropriately power the clinical trial may delay study completion. Extended timelines result in increased costs for the ongoing study and delays in downstream activities, which may include additional clinical trials or a potential regulatory submission. We present an implementation of the Anisimov and Fedorov (2007) algorithm to predict remaining recruitment time in JMP Clinical. The arrival of patients at each center follows an independent Poisson process with rate sampled from a gamma distribution with unknown parameters. Given current enrollment information, model parameters are estimated using maximum likelihood, which are then used to predict the remaining recruitment time and confidence intervals through simulation. Further, if there is a high probability of missing the recruitment deadline, the number of additional clinical centers needed to meet the deadline is determined, adaptively adjusting the number of centers until the target time is achieved.