ENHANCING HEALTHCARE INTEROPERABILITY THROUGH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: A PREDICTIVE ANALYTICS FRAMEWORK FOR UNIFIED PATIENT CARE
Praveen Kumar Reddy Gujjala
Abstract
Healthcare interoperability remains a critical challenge in modern medical systems, hindering seamless data exchange and comprehensive patient care.This study presents a novel framework integrating Artificial Intelligence (AI) and Machine Learning (ML) with predictive analytics to enhance healthcare interoperability across disparate medical systems.Our implementation utilized advanced ML algorithms including Random Forest, Support Vector Machines, and Neural Networks to create intelligent data mapping, semantic interoperability, and predictive care coordination models.The framework was deployed across a network of 15 healthcare institutions serving 2.8 million patients over 18 months.Results demonstrated a 78% improvement in data standardization accuracy, 85% reduction in manual data reconciliation efforts, and 67% enhancement in predictive care alerts across integrated systems.The AI-driven approach successfully addressed semantic heterogeneity, temporal data alignment, and Enhancing Healthcare Interoperability Through Artificial Intelligence and Machine Learning: A Predictive Analytics Framework for Unified Patient Care https://iaeme.com/Home/journal/IJCET182 [email protected] care continuity challenges.Novel contributions include an adaptive learning algorithm for dynamic schema mapping, a predictive risk stratification model for interoperable care alerts, and an intelligent data quality assessment framework.This research provides empirical evidence that AI/ML-enhanced interoperability can significantly improve healthcare delivery while reducing operational complexity and costs.