Impact Of Blockchain-Based Automation On Healthcare Ehr Systems: Efficiency, Scalability, And Patient Outcomes

Abstract

The integration of blockchain technology and hybrid deep learning techniques in healthcare systems is a ground-breaking approach that aims to improve scalability, security, and patient care. Blockchain provides a decentralized, transparent framework for secure data storage, sharing, and access control, ensuring data integrity, privacy, and interoperability. Hybrid deep learning, combining deep learning algorithms with traditional machine learning approaches, enables accurate and efficient processing of complex healthcare data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, ensuring only authorized entities can access and modify sensitive health information. The framework also enables real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning offers numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision-making. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient data management, personalized patient care, and advancements in medical research.