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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 pursue two supervised learning approaches: (1) We directly predict the surgical case durations using features derived from electronic medical records and from hospital operational information. For this regression problem, we propose a novel metric for measuring accuracy of predictions which captures key issues relevant to hospital operations. We evaluate several 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 our semi-automated methods can outperform surgeons by a substantial margin. (2) We consider a classification problem in which each prediction provided by a surgeon is predicted to be correct, an overestimate, or an underestimate. This classification mechanism builds on the metric mentioned above and could potentially be useful for detecting human errors. Both supervised learning approaches give insights into the feature engineering process while creating the basis for decision support tools.

Improving predictions of pediatric surgical durations with supervised learning