Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The convergence of comprehensive electronic health record systems with advanced cloud-based artificial intelligence capabilities represents a transformative paradigm shift in population health management and clinical care delivery. This technical review explores the integration framework that enables healthcare organizations to transition from reactive episodic care models toward proactive, data-driven healthcare delivery systems. The integration encompasses sophisticated machine learning operations frameworks, predictive analytics applications, and advanced AI capabilities, including large language models and computer vision technologies. Implementation strategies address critical operational challenges, including clinician workflow integration, system interoperability, and ethical considerations surrounding algorithmic bias and model explainability. The review demonstrates how cloud-based predictive analytics platforms can identify at-risk patients through readmission prediction models, chronic disease risk identification systems, and emergency department surge forecasting capabilities. Advanced natural language processing enables automated clinical documentation enhancement while maintaining regulatory compliance with healthcare privacy requirements. Edge computing integration provides real-time analytics capabilities at the point of care while preserving data privacy through local processing architectures. Organizational transformation strategies emphasize the importance of data-driven culture development, workforce capability building, and systematic implementation roadmaps that balance innovation objectives with operational stability requirements.