Application of healthcare data mining techniques to planning for nursing length of stay in surgical departments
Systems and Soft Computing, vol.9, 2026 (ESCI, Scopus)
- Publication Type: Article / Article
- Volume: 9
- Publication Date: 2026
- Doi Number: 10.1016/j.sasc.2026.200535
- Journal Name: Systems and Soft Computing
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
- Keywords: Artificial neural networks, Data mining, Health, Length of stay, Machine learning, Nursing planning
- Azerbaijan State University of Economics (UNEC) Affiliated: Yes
Abstract
Effective allocation of nurse resources in surgical departments is essential for improving patient care and controlling operating costs in a health society. Length of stay (LOS) is the metric that connects clinical workload to staffing decisions, yet ward-level forecasting and its translation into daily nursing schedules remain limited. This study presents a hybrid, data-driven decision-support system that combines machine-learning LOS prediction with Reinforcement Learning (RL) for the surgical ward. A dataset of 137,145 records is used to evaluate Random Forest, Gradient Boosting, Decision Tree, and a Multi-layer Perceptron. Random Forest achieved the most accurate and stable performance (R² = 0.84; RMSE = 1.63), and its predicted LOS states drive an RL agent that adjusts staffing and triggers early-discharge reviews. The novelty lies in focusing on the understudied surgical ward, converting predicted LOS into a daily scheduling policy, and integrating forecasting with RL-based scheduling. The hybrid model reduced average LOS from 6.12 to 4.82 days, lowered weekly nurse overtime by approximately 47%, and improved staff utilization.