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Optimizing surgical efficiency: predicting case duration of common general surgery procedures using machine learning.

Michelle Kwong^1,2, Mohammad Noorchenarboo³, Katarina Grolinger³

¹Department of Anesthesiology and Pain Medicine, University of Alberta, Edmonton, Canada.

Surgical Endoscopy

|June 26, 2025

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This summary is machine-generated.

Machine learning models, specifically Artificial Neural Networks (ANN), accurately predict surgical duration, outperforming subjective surgeon estimates. This advancement can optimize operating room resource utilization by improving case time predictions.

Area of Science:

Health Informatics
Machine Learning in Surgery
Operating Room Management

Background:

Accurate prediction of surgical duration is crucial for efficient operating room resource allocation.
Current scheduling relies on subjective surgeon estimates, which can be inaccurate.
Developing objective prediction models is essential for optimizing surgical scheduling.

Purpose of the Study:

To develop and compare various predictive models, including machine learning algorithms, for estimating surgical case duration.
To objectively predict case duration for common elective general surgical procedures.
To benchmark model performance against traditional surgeon estimates.

Main Methods:

Trained predictive models using electronic health record data from three academic tertiary centers.

Keywords:

Case duration prediction Case scheduling Elective surgery General surgery Machine learning

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Defined 'case time duration' as the time from patient entry to operating room departure.

Evaluated models based on predictive accuracy and residual analysis, comparing them to 'scheduled duration' (surgeon's estimate).

Main Results:

Trained multiple models (linear regression, SVM, Random Forest, XGBoost, ANN) on 16,159 patients undergoing 17,246 procedures.
The Artificial Neural Network (ANN) model showed superior predictive accuracy with a Root Mean Squared Error of 49.7 min and Mean Absolute Error of 31.8 min.
ANN provided a more accurate case time estimation, exceeding surgeon estimates by over 18 minutes on average.

Conclusions:

ANN model estimates of surgical case time are significantly more accurate than provider knowledge-based estimates.
Machine learning models can eliminate subjective bias inherent in traditional surgical scheduling methods.
Future applications of machine learning in predicting case duration can enhance healthcare resource utilization.