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A novel approach to forecast surgery durations using machine learning techniques.

Marco Caserta1, Antonio García Romero2

  • 1IE Business School, IE University, Paseo de la Castellana 259E, Madrid, 28046, Madrid, Spain. marco.caserta@ie.edu.

Health Care Management Science
|July 10, 2024
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Summary

This study introduces a machine learning (ML) model to predict surgical procedure duration by analyzing team dynamics. The new model significantly improves prediction accuracy, aiding hospital operational planning.

Keywords:
Feature importanceMachine learningSurgery durationTeam compositionTeam dynamics

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Area of Science:

  • Healthcare Operations Research
  • Machine Learning in Medicine
  • Surgical Workflow Optimization

Background:

  • Accurate prediction of surgical procedure duration is crucial for efficient operating room scheduling and resource allocation.
  • Current prediction methods often overlook critical human factors within surgical teams.
  • Understanding team dynamics can significantly impact surgical outcomes and efficiency.

Purpose of the Study:

  • To develop and validate a novel machine learning (ML) methodology for predicting surgical procedure duration.
  • To investigate the impact of surgical team dynamics and composition on prediction accuracy.
  • To improve upon existing models by incorporating team-specific variables.

Main Methods:

  • Utilized a comprehensive dataset of over 77,000 surgical procedures.
  • Developed and applied machine learning techniques incorporating predictors related to surgical team experience, familiarity, social behavior, and gender diversity.
  • Compared the performance of the new ML model against a baseline model mimicking current decision-making approaches.

Main Results:

  • Achieved a 24% improvement in mean absolute error (MAE) compared to the baseline model.
  • Demonstrated the significant contribution of surgeon experience and team composition dynamics to prediction accuracy.
  • Validated the effectiveness of the proposed ML methodology in predicting surgical duration.

Conclusions:

  • The developed ML methodology, incorporating surgical team dynamics, offers superior accuracy in predicting procedure duration.
  • Enhanced prediction accuracy can lead to more efficient hospital operational planning and resource management.
  • Integrating insights into team composition can optimize operating room utilization and improve overall healthcare delivery.