HEALTHCARE DATA ANALYTICS: LEVERAGING PREDICTIVE ANALYTICS FOR IMPROVED PATIENT OUTCOMES

Abstract

Predictive analytics has emerged as a transformative force in modern healthcare, revolutionizing patient care management by integrating artificial intelligence and machine learning technologies. This comprehensive article examines the implementation, challenges, and outcomes of predictive analytics across healthcare facilities worldwide. The article explores diverse data sources, including electronic health records (EHRs), wearable technology, insurance claims, genomic information, and patient-reported outcomes, highlighting their role in improving clinical decision-making. Advanced analytics techniques, ranging from traditional statistical models to sophisticated machine learning algorithms, have significantly improved disease prediction, treatment optimization, and resource allocation. The implementation framework encompasses data management, model development, and deployment strategies while addressing critical challenges in data privacy, system interoperability, and clinical acceptance. Real-world success stories across sepsis detection, cancer care, and chronic disease management showcase the tangible benefits of predictive analytics in healthcare settings. This article underscores the transformative potential of predictive analytics as a cornerstone of data & analytics-driven healthcare, aiming to foster scalable, patient-centered solutions that enhance the quality and accessibility of care.