LEVERAGING AI AND PREDICTIVE ANALYTICS IN CLOUD-BASED HEALTHCARE SYSTEMS FOR OPTIMIZED PATIENT CARE MANAGEMENT
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Keywords
Artificial Intelligence, Predictive Analytics, Cloud-Based Healthcare Systems, Patientcare Management, Machine Learning, Big Data Analytics, Personalized Healthcare
Abstract
Introduction: This research examines the potential of cloud-based healthcare systems, enhanced by artificial intelligence (AI) and predictive analytics, to improve patient care. With increasing demand for more efficient healthcare solutions, AI-driven systems offer promising advancements in real-time health monitoring, risk assessments, and personalized treatment recommendations. By integrating machine learning algorithms like random forests, support vector machines (SVMs), and gradient boosting, these systems predict patient health trends and identify high-risk individuals. This research aims to demonstrate how AI-powered frameworks can optimize resource allocation, improve health outcomes, reduce costs, and enhance overall patient satisfaction.
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Objectives: This research are to develop AI-driven cloud-based healthcare systems for real-time patient monitoring, risk assessment, personalized treatment recommendations, and improved resource allocation to enhance patient care.
Methods: This research employs a multi-stage methodology to evaluate the potential of AI-driven cloud-based healthcare systems. First, a comprehensive literature review is conducted to explore current advancements in healthcare technologies, AI algorithms, and cloud infrastructures. Next, various machine learning algorithms, including random forests, support vector machines (SVMs), and gradient boosting, are developed and integrated into predictive analytics frameworks. These algorithms process large-scale healthcare datasets to predict patient health, risk factors, and disease progression. The developed systems are tested in cloud environments to assess their effectiveness in real-time health monitoring, risk assessment, and targeted treatment suggestions.
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Results: AI-driven cloud-based healthcare systems significantly improve patient care. Predictive analytics frameworks, utilizing random forests, SVMs, and gradient-boosting algorithms, accurately forecast patient outcomes, identify high-risk individuals, and predict disease progression. Real-time health monitoring and risk assessment enabled by these systems allow healthcare providers to deliver more targeted treatment recommendations and optimize resource allocation. The research shows potential for enhanced patient outcomes, reduced healthcare costs, and improved patient satisfaction. These findings confirm that combining AI and cloud-based systems can transform the healthcare industry by offering data-driven, personalized care solutions.
Conclusions: The cloud-based healthcare systems powered by AI and predictive analytics can significantly enhance patient care. By enabling real-time monitoring, accurate risk assessment, and personalized treatments, these systems improve health outcomes, reduce costs, and increase patient satisfaction, offering a transformative approach to modern healthcare management.
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Dr. Prasanth Kamma
Principal Architect (Center of Excellence), Aetna Inc. (A CVS Health Company), CT, USA