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Abstract
Effective workforce management in healthcare is critical for ensuring optimal patient care, resource efficiency, and staff well-being. Healthcare workforce analytics leverages data-driven insights to enhance staffing strategies, predict demand, and improve resource utilization. This study reviews various analytical techniques, including predictive modeling, machine learning, and data visualization, used to optimize staffing levels, reduce workforce-related costs, and enhance patient outcomes. Through the application of workforce analytics, healthcare organizations can better align their human resources with patient demand, thereby minimizing understaffing or overstaffing, reducing burnout, and improving patient satisfaction. The paper also explores challenges in implementing workforce analytics, such as data integration, privacy concerns, and the need for organizational buy-in. By focusing on these advancements, this study provides a framework for utilizing workforce analytics to meet the dynamic needs of the healthcare industry.
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