AI-Driven Optimization of Healthcare Databases for Intelligent Patient Data Management

Abstract

Artificial Intelligence (AI) is fundamentally transforming healthcare data management by enabling intelligent automation, predictive analytics, and data-driven clinical decision support systems. This article explores AI-driven optimization in healthcare databases, examining how machine learning and deep learning algorithms revolutionize the collection, organization, and utilization of patient data. The proposed framework integrates five interconnected components—data acquisition and integration, preprocessing and quality enhancement, machine learning model development, deep learning for personalized care, and real-time decision support—to create a cohesive ecosystem for intelligent patient data management. Implementation results demonstrate significant improvements in clinical outcomes, operational efficiency, and cost reduction, with AI systems achieving diagnostic performance comparable to or exceeding human specialists. However, successful deployment requires careful consideration of ethical challenges, including data privacy and security, algorithmic bias and health equity, explainability and clinical trust, and integration with existing legacy infrastructure.