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Journal of Medical Systems

, 43:32 | Cite as

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

  • Beiqun ZhaoEmail author
  • Ruth S. Waterman
  • Richard D. Urman
  • Rodney A. Gabriel
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durations. In this study, we sought to use machine learning to develop an accurate predictive model for RAS case duration. We analyzed a random sample of robotic cases at our institution from January 2014 to June 2017. We compared the machine learning models to the baseline model, which is the scheduled case duration (determined by previous case duration averages and surgeon adjustments). Specifically, we used: 1) multivariable linear regression, 2) ridge regression, 3) lasso regression, 4) random forest, 5) boosted regression tree, and 6) neural network. We found that all machine learning models decreased the average root-mean-squared error (RMSE) as compared to the baseline model. The average RMSE was lowest with the boosted regression tree (80.2 min, 95% CI 74.0–86.4), which was significantly lower than the baseline model (100.4 min, 95% CI 90.5–110.3). Using boosted regression tree, we can increase the number of accurately booked cases from 148 to 219 (34.9% to 51.7%, p < 0.001). This study shows that using various machine learning approaches can improve the accuracy of RAS case length predictions, which will increase utilization of this limited resource. Further work is needed to operationalize these findings.

Keywords

Robot-assisted surgery Machine learning OR efficiency Health economics Prediction Case duration 

Notes

Acknowledgements

Dr. Beiqun Zhao is funded by the National Library of Medicine Training Grant: NIH grant T15LM011271.

Funding

This study was funded by the National Library of Medicine Training Grant (T15LM011271).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of SurgeryUniversity of California, San DiegoLa JollaUSA
  2. 2.Department of Medicine, Division of Biomedical InformaticsUniversity of California, San DiegoLa JollaUSA
  3. 3.Department of AnesthesiologyUniversity of California, San DiegoLa JollaUSA
  4. 4.Department of Anesthesiology, Perioperative and Pain MedicineBrigham and Women’s HospitalBostonUSA

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