Leveraging big data in health care and public health for AI driven talent development in rural areas

Abstract

Introduction: This study proposes a novel Transformer-based approach to enhance talent attraction and retention strategies in rural public health systems. Motivated by the persistent shortage of skilled professionals in underserved areas and the limitations of traditional recruitment methods, we leverage big data analytics and natural language processing to address workforce distribution imbalances. Methods: By analyzing diverse data sources such as social media, surveys, and job satisfaction reports, the Transformer model identifies complex, context-specific factors influencing candidate preferences, including career advancement opportunities, lifestyle alignment, and community engagement. Results: Our framework offers a personalized, data-driven mechanism to match healthcare professionals with rural roles effectively. Experimental results demonstrate significant improvements in recruitment precision and retention forecasting. Discussion: This work contributes a scalable and adaptive solution to rural healthcare workforce challenges, offering valuable insights for policy-makers and public health organizations aiming to revitalize rural health services.

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