Efficient workforce management is critical for ensuring the quality, safety, and sustainability of hospital operations. Traditional human resource management (HRM) approaches often rely on manual processes that are prone to errors, lack adaptability, and fail to adequately balance staff preferences with patient care requirements. To address these challenges, this research proposes an AI-driven HRM framework for hospitals that integrates forecasting, optimization, and performance evaluation to enhance workforce planning, staff scheduling, and continuous assessment. The framework comprises three core modules: (i) workforce demand forecasting, leveraging machine learning models such as LSTM, XGBoost, and Random Forest to predict patient admissions and staffing needs; (ii) intelligent staff scheduling, employing optimization models under legal, contractual, skill-based, and preference-aware constraints to generate equitable and efficient rosters; and (iii) performance evaluation, combining structured metrics (task completion, attendance, punctuality) with unstructured feedback (patient surveys, peer reviews) analyzed using natural language processing. Extensive experiments were conducted using both synthetic and real hospital datasets. Results show that the proposed approach outperforms conventional methods, with LSTM achieving the highest forecasting accuracy (MAE = 6.1, R2 = 0.91), and the scheduling module reducing conflicts by 41% while improving fairness (Gini coefficient = 0.08). The performance evaluation framework further revealed 74% positive patient feedback and highlighted actionable insights for administrators. Stress tests confirmed scalability, with solver times remaining under 95 s for 1000 staff members. Pilot deployments demonstrated tangible benefits, including an 18% reduction in patient waiting times and a 14% improvement in satisfaction scores. Overall, the framework demonstrates strong potential for advancing hospital workforce management by improving efficiency, fairness, and quality of care.
Wang et al. (2026) studied this question.