Strategic Workforce Planning in Hospital Systems Through Machine-Learning-Based Forecasting of Staffing Demand and Skill Mix

Abstract

Healthcare organizations face growing challenges in aligning workforce supply with fluctuating patient demand, especially as labor shortages and operational pressures intensify. Traditional staffing models often lack the flexibility and predictive power needed to support long-term strategic planning. This paper presents an innovative approach to strategic workforce planning in hospital systems through advanced machine learning techniques. We develop a comprehensive mathematical framework that integrates time series forecasting, multi-objective optimization, and deep learning architectures to predict staffing demands and optimize skill mix allocations. The model incorporates temporal patterns of patient acuity, departmental workload fluctuations, and staff availability constraints to generate robust predictions across multiple planning horizons. Our methodology combines convolutional neural networks with transformer architectures to capture both local and global temporal dependencies in historical workforce data, while employing Gaussian process regression to quantify uncertainty in predictions. Validation across five hospital systems demonstrates that our approach reduces mean absolute percentage error in staff requirement forecasts by 27.4\% compared to traditional methods, while simultaneously improving scheduling efficiency by 18.2\% and reducing projected labor costs by 12.6\%. The system's adaptive forecasting capabilities enable dynamic reallocation of human resources in response to shifting demand patterns, providing hospital administrators with actionable intelligence for strategic workforce planning while maintaining high-quality patient care standards.