Abstract
AbstractIn the rapidly evolving landscape of digital systems and modern technology, including computing and device peripherals, a paradigm shift is evident across various engineering fields. This transformation is characterized by the integration of cutting‐edge concepts such as Artificial Intelligence (AI), Machine Learning, Deep Learning, Cloud Computing, and Smart Digital Systems. Notably, these advancements hold significant promise in Biomedical Engineering, where they play crucial roles in comprehending human conditions and anatomy. Given the paramount importance of health, especially amidst rising diseases and health‐related challenges, there arises a pressing need for optimal solutions. This research focuses on the integration of Electronic Health Record (EHR) systems with AI to deliver efficient solutions. Specifically, the research targets issues related to prioritization and segment queue management, with the aim of enhancing overall proficiency and efficiency in healthcare operations. The research presents a prototype version developed, deployed, and experimentally evaluated to assess its performance in achieving the stated objectives. Through this investigation, the research seeks to contribute to the advancement of EHR systems and AI integration in healthcare, ultimately aiming to enhance patient care and healthcare delivery processes.
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