Park Jung-Bin, Roh Gyun-Ho, Kim Kwangsoo, Kim Hee-Soo
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Interdisciplinary Program of Medical Informatics, Seoul National University, Seoul, Republic of Korea.
J Med Syst. 2025 Jan 14;49(1):8. doi: 10.1007/s10916-025-02141-y.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R) metrics. Our findings highlighted that Random Forest models excelled in department-specific applications, achieving an MAE of 16.32, an RMSE of 31.19, and an R of 0.92, significantly outperforming general models and conventional estimates. This improvement emphasizes the advantage of customizing models to fit the distinct characteristics and data patterns of each department. Additionally, our SHAP-based feature importance analysis identified morning operation timing, ICU ward assignments, operation codes, and surgeon IDs as key factors influencing surgical duration. This suggests that a detailed and nuanced approach to model development can substantially increase prediction accuracy. By providing a more accurate, reliable tool for predicting surgical case durations, our department-specific Random Forest models promise to enhance surgical scheduling, leading to more effective OR management. This approach underscores the importance of leveraging tailored, data-driven models to improve healthcare outcomes and operational efficiency.
优化手术室（OR）利用率对于提升医院管理和运营效率至关重要。准确预测手术时长对于实现这种优化必不可少。我们的研究旨在通过开发针对特定外科科室的随机森林模型，提高这些预测的准确性，超越传统估计方法。利用一个全面的数据集，我们应用了几种机器学习算法，包括随机森林、XGBoost、线性回归、LightGBM和CatBoost，并使用平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R）指标评估它们的性能。我们的研究结果表明，随机森林模型在特定科室的应用中表现出色，MAE为16.32，RMSE为31.19，R为0.92，显著优于通用模型和传统估计。这种改进强调了根据每个科室的不同特征和数据模式定制模型的优势。此外，我们基于SHAP的特征重要性分析确定了上午手术时间、重症监护病房分配、手术编码和外科医生ID是影响手术时长的关键因素。这表明，一种详细且细致入微的模型开发方法可以大幅提高预测准确性。通过提供一个更准确、可靠的手术时长预测工具，我们的特定科室随机森林模型有望改善手术安排，从而实现更有效的手术室管理。这种方法强调了利用定制的、数据驱动的模型来改善医疗结果和运营效率的重要性。
