Effective management of operating room resources relies on accurate predictions of surgical case durations. This prediction problem is known to be particularly difficult in pediatric hospitals due to the extreme variation in pediatric patient populations. We have developed a novel metric for measuring accuracy of predictions which captures key issues relevant to hospital operations. With this metric in mind we have proposed several tree-based prediction models. Some are automated (they do not require input from surgeons) while others are semi-automated (they do require input from surgeons). We see that many of our automated methods generally outperform currently used algorithms and even achieve the same performance as surgeons. Our semi-automated methods can outperform surgeons by a significant margin. This work provides insights into the predictive value of different features and suggests avenues of future work.