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Transforming Orthopaedic Trauma Care: Forecasting Operating Room Demand by Harnessing Time-Series Analysis and
Aazad Abbas1,2,3, Dharsan Ravindran2,3, Michael Simone4
1Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
The Journal of Bone and Joint Surgery. American Volume
|July 28, 2026
Summary
Accurate forecasting of daily orthopaedic trauma caseloads is now possible by integrating historical data with external factors. This predictive framework helps trauma centers optimize operating room scheduling and resource allocation for improved patient care.
Area of Science:
- Orthopaedic surgery
- Health systems science
- Data science
Background:
- Efficient operating room (OR) time allocation is crucial for trauma centers facing unpredictable patient volumes.
- Forecasting trauma volume is challenging, necessitating advanced predictive models.
- Integrating historical patterns with exogenous factors can enable proactive scheduling for daily operative demand.
Purpose of the Study:
- To develop and evaluate time-series and machine learning models for predicting daily orthopaedic trauma operative caseloads.
- To assess the accuracy of these models in forecasting OR time requirements.
- To provide a framework for optimizing OR scheduling and resource allocation in trauma centers.
Main Methods:
- Utilized historical patient data (January 2012-December 2023) for orthopaedic trauma surgeries.
- Developed time-series and machine learning models incorporating hospital, regional population, and environmental data.
- Trained models to minimize Mean Absolute Error (MAE) and evaluated prediction accuracy for 7-hour and 8-hour clinical volume thresholds.
Main Results:
- The Pattern Mining and Discovery Seasonal Autoregressive Integrated Moving Average (PMD-SARIMA) model achieved the lowest test MAE (1.80 hours), significantly outperforming rolling averages and intuitive models.
- Machine learning and time-series models accurately predicted daily caseload exceeding an 8-hour OR block 85% of the time.
- The developed models substantially outperformed rolling average baselines in predicting OR volume thresholds.
Conclusions:
- Integrating exogenous factors with historical trauma data enables accurate daily orthopaedic trauma caseload forecasting.
- This predictive framework can be calibrated by trauma centers to optimize OR scheduling, staffing, and resource allocation.
- Proactive resource allocation minimizes surgical delays and ensures timely, efficient care for trauma patients.