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Data-driven decision support in hospital resource planning: an artificial intelligence-based model proposal for
Emin Demir1, Yusuf Ziya Ayik2, Muhammet Özbilen3
1Atatürk University, Institute of Social Sciences, Department of Management and Information Systems, Erzurum, Turkey.
International Journal of Medical Informatics
|May 17, 2026
Summary
Meteorological factors significantly impact emergency department visits. An explainable artificial intelligence model, using dynamic updates, improves prediction accuracy for better hospital resource planning.
Area of Science:
- Healthcare Management
- Artificial Intelligence
- Environmental Health
Background:
- Healthcare system sustainability relies on accurate resource planning, particularly in unpredictable emergency departments.
- This study investigates the influence of meteorological factors on emergency department (ED) visits.
- A large dataset from two Turkish public hospitals was utilized.
Purpose of the Study:
- To analyze the impact of meteorological factors on ED visits.
- To develop a highly accurate and explainable AI-based decision support model for hospital management.
- To enhance operational efficiency through proactive resource planning.
Main Methods:
- Comprehensive feature engineering, including calendar variables, meteorological lags, and historical trends.
- Variable selection using Correlation, Granger Causality, and SHAP (SHapley Additive exPlanations) for explainable AI (XAI).
- Comparative analysis of 22 models across Machine Learning, Deep Learning, and Time Series categories.
Main Results:
- A 7-day Walk-Forward (WF-7d) update scenario proved optimal, reducing average error by 9.62% compared to static models.
- The Prophet model achieved the best performance (e.g., 5.54% MAPE at Ordu State Hospital).
- Support Vector Machine (SVM) and CatBoost models demonstrated generalizability with low error rates.
Conclusions:
- The developed AI system offers a proactive decision support mechanism for hospital administrators.
- Potential applications include optimizing staff scheduling and bed capacity management.
- The system can improve overall operational efficiency in healthcare settings.
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