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

Aug 5, 2026

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Accurate surgical case duration prediction is critical for optimizing pediatric healthcare operations. Machine learning models were developed to predict pediatric surgical case duration using novel non-clinical features and benchmarked against existing scheduling estimates. Using 202,149 records from Children’s Health Dallas, operational context, team familiarity, and surgeon experience were combined with clinical variables. LightGBM achieved the best performance, markedly reducing median prediction error and highlighting the importance of non-clinical factors for multiple specialties and ages.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

1. Introduction

1.1 Machine Learning Approaches in Surgery Duration Prediction

Proceedings of the 59th Hawaii International Conference on System Sciences | 2026 Predicting Pediatric Surgical Case Duration Using Machine Learning: Leveraging Team Dynamics and Operational Features | Katherine Adams | Yang Yang | Gian-Gabriel Garcia | Arkajyoti Roy | | --- | --- | --- | --- | | University of Texas at San Antonio | Georgia Institute of Technology | University of Washington | University of Texas at San Antonio | | [katherine.adams@utsa.edu](katherine.adams@utsa.edu) | [yyang898@gatech.edu](yyang898@gatech.edu) | [garciagg@uw.edu](garciagg@uw.edu) | [arkajyoti.roy@utsa.edu](arkajyoti.roy@utsa.edu) | | Dai Chung | Xin-Li Leong | Vishal Ahuja | | --- | --- | --- | | Children’s Health, University of Texas Southwestern | Axxess | Southern Methodist University, University of Texas Southwestern | | [dai.chung@childrens.com](dai.chung@childrens.com) | [xleong@mail.smu.edu](xleong@mail.smu.edu) | [vahuja@smu.edu](vahuja@smu.edu) | 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 incorporatedfeatures 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. Keywords: Surgery scheduling, machine learning, predictive modeling, healthcare operations, reliability 1. Introduction Building reliable surgical schedules represents one of the most critical operational challenges in pediatric healthcare systems. The financial implications are substantial—robust schedules enable maximizing profits through higher operating room (OR) utilization, more efficient staffing allocation, and increased patient throughput. However, the consequences of unreliable scheduling extend far beyond financial considerations. Healthcare providers with unpredictable schedules face increased workloads, inefficient time utilization, unfair case distribution, and elevated risk of medical errors (Feeley et al., 2024; Gates et al., 2018). From the patient perspective, unreliable scheduling translates to reduced quality of care and prolonged fasting and waiting periods, particularly problematic for infants and young children (Dulay et al., 2024; Li et al., 2020) . The foundation of efficient surgical scheduling lies in obtaining precise case duration estimates – specifically, the time from patient entry into the OR ("wheels in") to departure ("wheels out") . Without accurate estimates, surgery schedules systematically over- or under-estimate procedural completion times, creating substantial operational inefficiencies given OR costs of approximately $36–62 per minute (Childers et al., 2018; Macario, 2010) . Pediatric surgery duration prediction presents unique challenges compared to adult procedures. Pediatric cases exhibit higher variability and less predictable relationships to physician experience factors (Bravo et al., 2015; McAteer et al., 2013) . While some researchers have examined duration patterns for specific procedure types (Smallman & Dexter, 2010), comprehensive understanding of factors affecting pediatric surgery duration remains limited.

FAQs

Why is predicting pediatric surgical case duration important in healthcare operations?

Accurate duration estimates improve surgical schedule reliability, supporting better OR utilization, staffing efficiency, and patient throughput. Unreliable scheduling can increase workload, waiting times, and risk of errors while reducing care quality.

What data and features were used to build the machine learning prediction models?

The models used 202,149 surgical records from Children’s Health Dallas, combining clinical variables with non-clinical operational context such as team familiarity and surgeon experience.

Which machine learning model performed best and what was the key improvement?

LightGBM performed best, reducing median prediction error from −12.00 to −0.69 minutes. Five of the top ten features were non-clinical, indicating operational factors substantially influence predicted duration.

Aug 5, 2026

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