HOSPITAL MANAGEMENT SYSTEM: HUMAN RESOURCES 4 (COMPENSATION MANAGEMENT AND BENEFITS ADMINISTRATION) WITH AI PREDICTIVE ANALYTICS AND MACHINE LEARNING FOR INTEGRATED OVERTIME, INCENTIVES AND BENEFITS FORECASTING

Abstract

Effective human resource management is essential for healthcare operational efficiency and employee well-being. However, compensation management and benefits administration remain highly resource-intensive for HR departments. This often results in inefficiencies, miscalculations in compensation, and delays in administering benefits. The objective of this project is to develop an AI-driven HR system that automates compensation and benefits administration, forecasts overtime hours and incentives, reduces administrative workload, and supports better decision-making for HR personnel. The HMS is developed using the Agile Scrum methodology, which involves recurring cycles of planning, development, testing, and feedback. Each sprint focuses on features such as real-time compensation tracking, overtime and incentives forecasting, and benefits and leave administration. The system is built on the MERN tech stack, using JavaScript for coding and MongoDB for database management. Its core feature leverages historical HR data, drawing on employee records to forecast overtime hours and benefits eligibility. By applying statistical and computational techniques to this data, the system generates predictions that help HR departments make better decisions and allocate resources more effectively. The system significantly enhanced HR management in compensation and benefits. The overtime forecasting model accurately predicted overtime requirements, enabling HR practitioners to plan resources more effectively. It also simplified tracking and ensured the timely delivery of employee benefits. Furthermore, the system’s predictive functions and efficient data management minimized the need for manual effort, allowing HR personnel to focus on more strategic functions. The project successfully developed an AI-driven HMS that automates core HR functions, improving overtime forecast accuracy, reducing administrative burdens, and increasing HR personnel productivity. Its seamless integration with existing hospital infrastructure ensures minimal disruption during implementation. Although the outcomes are positive, regular updates to the system and AI models are necessary to address evolving healthcare needs. In conclusion, the system represents a significant step forward in the redesign of HR functions, contributing to improved patient care, greater employee satisfaction, and enhanced operational performance.