HOSPITAL MANAGEMENT SYSTEM CORE 1: (INPATIENT AND OUTPATIENT CARE THROUGH PREDICTIVE ANALYTICS WITH PATIENT RISK PREDICTION, LENGTH OF STAY PREDICTION, PREDICTIVE SCHEDULING AND FOLLOW UP NEEDS)

Abstract

The rapid growth of the healthcare industry is driven by advancements in medical technology and a continued rise in patient demand for high-quality care. However, current hospital systems often lack the capacity for real-time predictive capabilities, creating a gap in proactive healthcare management. This study addresses these challenges by introducing a Hospital Management System (HMS) that incorporates predictive analytics to support both inpatient and outpatient care. By leveraging machine learning, the system aims to detect potential health issues early, optimize scheduling accuracy, and recommend effective treatment strategies. This research provides a timely and innovative solution to enhance patient outcomes and improve operational efficiency in modern healthcare settings. Our project adopts the Agile methodology, which is highly effective for managing complex software development due to its adaptability and iterative structure. The process begins with the planning phase, where client data is gathered to establish a strong foundation and ensure the system aligns with specific requirements. Development then moves forward by translating these needs into functional components, allowing for continuous integration, testing, and refinement throughout the project lifecycle. The primary objective of this project is to optimize both inpatient and outpatient care services by integrating advanced predictive analytics and automated workflows. The system is designed to enhance hospital efficiency, elevate the quality of patient care, and streamline administrative operations through data-driven insights and real-time monitoring. Key focus areas include patient risk prediction, length of stay forecasting, predictive scheduling, and follow-up care management, each contributing to more informed decision-making and improved healthcare outcomes. Integrating predictive analytics into hospital care has the potential to revolutionize patient treatment by shifting from a reactive to a proactive approach. Hospitals can anticipate potential patient risks, more accurately estimate length of stay, and optimize appointment scheduling, ensuring continuity of care even after discharge. This data-driven approach supports healthcare professionals in making faster, more informed decisions, ultimately enhancing operational efficiency and improving the overall patient experience.