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Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

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Optimizing operating room (OR) scheduling with AI predictive models significantly reduces surgery duration discrepancies and delays. New models improve efficiency for both elective and acute cardio-thoracic surgeries, enhancing patient flow and OR utilization.

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Area of Science:

  • Health Informatics
  • Operations Research
  • Surgical Planning

Background:

  • Effective operating room (OR) capacity management is crucial for minimizing surgery cancellations and long waiting lists.
  • Current surgery scheduling relies on average procedure times, often leading to discrepancies between planned and actual surgery durations.
  • These discrepancies negatively impact clinical and financial outcomes, as well as patient and staff satisfaction.

Purpose of the Study:

  • To quantify the discrepancy of current surgery scheduling models.
  • To develop and evaluate novel predictive models for optimizing surgery duration estimation.
  • To enhance operating room utilization and patient flow through improved scheduling.

Main Methods:

  • Utilized deidentified data from 2294 cardio-thoracic surgeries.
  • Calculated discrepancies using the existing surgeon's average procedure time model.
  • Developed and compared ensemble models including linear regression, random forest, and extreme gradient boosting.

Main Results:

  • Ensemble models reduced Root Mean Square Error (RMSE) for elective surgeries by 19% (0.99 vs 0.80) and for acute surgeries by 52% (1.87 vs 0.89).
  • The proportion of surgeries falling behind schedule decreased by 28% for elective (60% vs. 32%) and 9% for acute cases (37% vs. 28%).
  • Improvements were attributed to the inclusion of patient and surgery-specific features in the predictive models.

Conclusions:

  • Advanced AI-driven predictive models offer a significant improvement over traditional methods for surgery duration estimation.
  • These models can serve as valuable patient flow AI decision support tools for surgery planners.
  • Optimized OR scheduling through these predictive models leads to enhanced efficiency and resource utilization.