Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation

Hamed Zaribafzadeh, Wendy L. Webster, Christopher J. Vail, Thomas Daigle, Allan D. Kirk, Peter J. Allen, Ricardo Henao, Daniel M. Buckland

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations. Background: The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources. Methods: We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient-boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution. Results: The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length from August to December 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer underpredicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only overpredicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer underpredicted cases. Conclusions: We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.

Original languageEnglish (US)
Pages (from-to)890-895
Number of pages6
JournalAnnals of Surgery
Volume278
Issue number6
DOIs
StatePublished - Dec 1 2023

Bibliographical note

Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.

Keywords

  • implementation
  • machine learning
  • prospective analysis
  • surgical case duration
  • surgical case length

ASJC Scopus subject areas

  • Surgery

Fingerprint

Dive into the research topics of 'Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation'. Together they form a unique fingerprint.

Cite this