Predicting Pediatric Surgical Case Duration Using Machine Learning: Leveraging Team Dynamics and Operational Features

Abstract

Accurate surgical case duration prediction is critical for optimizing pediatric healthcare operations. We developed machine learning models (ML) to predict pediatric surgical case duration using novel non-clinical features and compared their performance to existing scheduling estimates. Using 202,149 surgical records from Children's Health Dallas, we incorporated features related to team familiarity, surgeon experience, and operational context alongside clinical variables. Among the ML models considered, LightGBM performed best, reducing median prediction error from −12.00 to −0.69 minutes. Five of the ten most important features were non-clinical, highlighting operational factors' significance. Performance gains were greatest in Otolaryngology (19.9%), Gastroenterology (19.8%), and Orthopedics (30.4%), and for patients aged 2–9 years (20.8%). These findings demonstrate that incorporating team dynamics and operational factors into ML models may significantly improve surgical duration predictions, supporting more accurate pediatric scheduling and potentially saving $407–$701 per case in operating room costs.

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