Predictive analytics of employee turnover at Sree Mookambika Institute of Medical Science, Kulasekaram.

Abstract

In the modern healthcare sector, employee retention has become a major challenge affecting organizational stability, service quality, and workforce efficiency. This study examines the role of predictive analytics in identifying and managing employee turnover at Sri Mookambikai Hospital. The research focuses on understanding how predictive analytics can help healthcare organizations identify factors influencing employee attrition and support effective retention strategies. Primary data were collected from employees using a structured questionnaire, while secondary data were gathered from journals, reports, articles, and academic sources. Statistical tools such as Key Influencer in Power BI, Multiple regression and factor analysis were used to analyze the collected data. The findings indicate that predictive analytics significantly helps in identifying turnover patterns, improving workforce planning, and enhancing employee retention. Factors such as workload, compensation, leadership support, career growth opportunities, and work environment were found to strongly influence employee turnover. However, challenges including data accuracy, employee adaptability, workload stress, and implementation difficulties remain. The study concludes that predictive analytics plays an important role in reducing employee turnover and improving organizational effectiveness in the healthcare sector, provided that proper HR strategies, employee engagement initiatives, and continuous monitoring systems are effectively implemented.