Construction of performance score dynamic prediction system for clinical departments using explainable machine learning.

Background: Accurate evaluation of clinical departmental performance is essential for public hospital management. However, existing approaches primarily rely on static, retrospective annual assessments and lack interpretability, limiting their ability to support early intervention and informed decision-making. To address these gaps, the study aimed to present a dynamic framework for predicting annual departmental performance score based on real-world hospital data using explainable machine learning. Methods: Performance data between January 2023 to December 2024 was collected from the Hospital Information System (HIS). Six machine learning models, namely Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting, XGBoost, CatBoost, were trained to predict annual performance scores across three cumulative time windows (January-March, January-June, January-September) with traning set of 2023 and testing set of 2024. Model Evaluation was using metrics such as R Results: A total of 648 records covering 24 months was obtained from 27 clinical departments. Compared with among models, LR model achieved the highest R Conclusion: The study presented a novel dynamic framework for clinical departmental performance prediction in tertiary public hospital using explainable machine learning. The framework supports progressive performance monitoring across multiple time windows and provides timely insights to inform managerial decisions. In addition, the development of the web tool helps facilitate practical application, offering methodological support and practical reference for performance management in public healthcare settings.