<b>AI and EHR Innovation: A Smart Approach to Medical Prioritization and Patient Care</b>

Abstract

With the rapid advancement of digital technologies, the convergence of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Cloud Computing is transforming the landscape of biomedical engineering and healthcare delivery. Among the persistent challenges in modern healthcare is the effective management of Electronic Health Records (EHRs), particularly in prioritizing patient cases, segmenting heterogeneous clinical data, and enabling timely, data-driven medical decisions. Existing EHR systems often suffer from fragmentation, inefficiency, and limited interoperability, which can delay diagnosis and treatment. This research proposes an AI-enhanced EHR framework designed to streamline healthcare workflows, improve information accessibility, and support clinical decision-making. The system integrates AI-driven algorithms for automated patient prioritization, intelligent data segmentation, and predictive analytics to enhance medical decision support. A functional prototype was developed, deployed, and tested in a simulated healthcare environment using real-world inspired datasets. The framework was implemented through a modular design, ensuring scalability and adaptability for various clinical contexts. Experimental evaluation demonstrated substantial improvements in response time, diagnostic accuracy, and system scalability compared to conventional EHR systems. The proposed solution addresses critical gaps in medical data management by enhancing efficiency, reducing clinician workload, and enabling faster, evidence-based decision-making. This study contributes to the growing body of work on intelligent healthcare systems, offering a practical, efficient, and scalable model for next-generation EHR integration. The findings underscore the transformative potential of AI-powered solutions in driving digital transformation and improving patient care outcomes in biomedical engineering.

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