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Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology.

Lauren E Miller¹, William Goedicke¹, Matthew G Crowson¹

¹Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.

Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery

|February 8, 2022

Summary

Machine learning models accurately predict otolaryngology surgical case durations, improving operating room efficiency. These advanced methods offer significant financial benefits by optimizing scheduling and resource allocation.

Keywords:

machine learning operating room scheduling otolaryngology resource utilization

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Area of Science:

Surgical Operations Research
Health Informatics
Machine Learning Applications

Background:

Optimizing operating room (OR) efficiency is crucial and relies on precise surgical case duration estimates.
Machine learning (ML) has shown promise in predicting OR case durations across various surgical subspecialties.
Existing non-ML techniques for estimating case lengths may lack the accuracy needed for optimal OR management.

Purpose of the Study:

To evaluate the effectiveness of ML methods in improving projected case lengths for otolaryngology-head and neck surgery.
To compare the predictive accuracy of ML models against traditional non-ML methods for OR case duration.
To identify the most effective ML algorithms for predicting surgical case times in otolaryngology.

Main Methods:

A retrospective review of 50,888 deidentified otolaryngology surgical cases from 2016-2020 at an academic institution.
Collection of preoperative variables including patient, surgeon, procedure, and facility data.
Evaluation of several ML algorithms (e.g., CatBoost, XGBoost) using root mean squared error and mean absolute error (MAE) for performance comparison against actual case durations.

Main Results:

The best-performing ML models, CatBoost and XGBoost, reduced the mean absolute error (MAE) for operative time prediction by 9.6 and 8.5 minutes, respectively, compared to current methods.
Key predictors of OR duration included the specific procedure, surgeon, subspecialty, and patient's postoperative destination.
CatBoost emerged as the top-performing ML model for predicting otolaryngology surgical case durations.

Conclusions:

Machine learning algorithms significantly enhance the accuracy of predicting OR case time durations in otolaryngology.
The integration of easily identifiable preoperative features beyond procedure and surgeon substantially improves prediction accuracy.
Improved case duration accuracy through ML is expected to yield considerable financial benefits for healthcare institutions.