This paper presents an approach to active learning that considers the non-independent and identically distributed (non-i.i.d.) structure of a clinical trial setting. There exists two types of clinical trials: retrospective and prospective. Retrospective clinical trials analyze data after treatment has been performed; prospective clinical trials collect data as treatment is ongoing. Traditional active learning approaches are often unrealistic in practice and assume the dataset is i.i.d. when selecting training samples; however, in the case of clinical trials, treatment results in a dependency between the data collected at the current and past visits. Thus, we propose prospective active learning to overcome the limitations present in traditional active learning methods, where we condition on the time data was collected. We compare our proposed method to the traditional active learning paradigm, which we refer to as retrospective in nature, on one clinical trial dataset and one non-clinical trial dataset. We show that in clinical trial settings, our proposed method outperforms retrospective active learning.