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A Machine Learning Solution to Predict Elective Orthopedic Surgery Case Duration.

Divya Sahadev¹, Thomas Lovegrove², Holger Kunz¹

¹University College London, Institute of Health Informatics.

Studies in Health Technology and Informatics

|July 1, 2022

Summary

This summary is machine-generated.

Machine learning models, Random Forest and XGBoost, accurately predict elective orthopedic surgery durations. These models demonstrated a 5% improvement over the current hospital scheduling system, enhancing surgical planning.

Keywords:

Machine learning Predictive Modelling Surgery Case Duration

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Area of Science:

Health Informatics
Machine Learning in Healthcare
Orthopedic Surgery Data Analysis

Background:

Accurate prediction of surgery durations is crucial for efficient operating room scheduling and resource allocation in hospitals.
Existing scheduling models may not fully leverage complex patient and procedural data for optimal predictive accuracy.
Elective orthopedic surgeries represent a significant volume of procedures, making them a key area for scheduling optimization.

Purpose of the Study:

To develop and compare machine learning models for predicting the duration of high-volume elective orthopedic surgeries.
To evaluate the performance of Random Forest regression and XGBoost regression against current hospital scheduling systems.
To identify advanced computational methods for improving surgical scheduling accuracy.

Main Methods:

Utilized a decade of surgical duration, patient demographic, and personnel data from 25,352 patients undergoing 15 high-volume elective orthopedic surgeries.
Implemented and compared two ensemble machine learning techniques: Random Forest regression (RF) and XGBoost (eXtreme Gradient Boosting) regression.
Validated model performance against the existing scheduling model at the East Kent Hospitals University NHS Foundation Trust (EKHUFT).

Main Results:

Both Random Forest and XGBoost models showed superior predictive accuracy for surgery durations.
The machine learning models outperformed the current hospital scheduling system by approximately 5%.
The study identified specific patient and procedural factors influencing surgery length.

Conclusions:

Ensemble machine learning methods, specifically RF and XGBoost, offer a significant improvement in predicting elective orthopedic surgery durations.
Implementing these advanced models can lead to more efficient operating room utilization and better healthcare resource management.
The findings support the integration of data-driven predictive analytics into hospital surgical scheduling processes.