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A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery.

Beiqun Zhao1,2, Ruth S Waterman3, Richard D Urman4

  • 1Department of Surgery, University of California, San Diego, 9300 Campus Point Drive, #7220, La Jolla, CA, 92037, USA. beiqunmzhao@gmail.com.

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|January 7, 2019
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Summary
This summary is machine-generated.

Machine learning models accurately predict robot-assisted surgery (RAS) case duration, improving operating room utilization. Boosted regression trees offered the lowest error, increasing accurately booked cases by over 34%.

Keywords:
Case durationHealth economicsMachine learningOR efficiencyPredictionRobot-assisted surgery

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Area of Science:

  • Surgical Technology
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • Robot-assisted surgery (RAS) demands significant capital investment, necessitating optimized operating room (OR) block time utilization.
  • Maximizing the use of each robotic unit is crucial for healthcare organizations to achieve a return on investment.
  • Accurate prediction of surgical case duration is a key factor in improving OR utilization.

Purpose of the Study:

  • To develop and evaluate machine learning models for accurate prediction of robot-assisted surgery case durations.
  • To compare the performance of various machine learning algorithms against a baseline predictive model.
  • To assess the potential impact of improved prediction accuracy on OR utilization.

Main Methods:

  • Analysis of a random sample of robot-assisted surgery cases from January 2014 to June 2017.
  • Implementation and comparison of six machine learning models: multivariable linear regression, ridge regression, lasso regression, random forest, boosted regression tree, and neural network.
  • Evaluation of model performance using the root-mean-squared error (RMSE) metric compared to a baseline model (scheduled case duration).

Main Results:

  • All tested machine learning models demonstrated a reduction in average RMSE compared to the baseline model.
  • The boosted regression tree model achieved the lowest average RMSE (80.2 min), significantly outperforming the baseline (100.4 min).
  • Utilizing the boosted regression tree model increased the proportion of accurately booked cases from 34.9% to 51.7% (p < 0.001).

Conclusions:

  • Machine learning approaches significantly enhance the accuracy of predicting RAS case lengths.
  • Improved prediction accuracy can lead to substantially increased utilization of valuable robotic surgical resources.
  • Further research is required to integrate these predictive models into clinical workflows for operationalization.