Year: 2025
Type: article
Abstract: In the evolving landscape of digital healthcare, the integration of artificial intelligence (AI) with secure data management technologies has become critical for enabling personalized, efficient, and reliable patient care. This study proposes a novel, multilayered electronic health record (EHR) framework that synergistically combines AI, blockchain, and cloud-edge computing to support predictive analytics, ensure data integrity, enhance scalability, and enforce secure access control. The architecture is composed of distinct layers, including stakeholder access, edge-level data acquisition, cloud-based AI-driven processing, integrated data management, and decentralized blockchain validation. To evaluate the system’s predictive capabilities, several machine learning models, Logistic Regression, Random Forest, and XGBoost were trained and fine-tuned using a healthcare dataset. Among these, the Logistic Regression model demonstrated superior performance, achieving the highest receiver operator characteristic-area under the curve score. Additionally, blockchain was employed to validate the integrity of EHR documents through the comparison of cryptographic hashes between original and tampered records. The results underscore the proposed framework’s robustness and intelligence, indicating strong potential for secure, real-time EHR management within digitally interconnected healthcare environments.
Authors: Ritu Singhal, Ananya Singhal, Ishita Singhal, Vikas Bansal
Institution: Indraprastha College for Women
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W4416099843