Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

This research paper delves into the burgeoning field of Big Data analytics in healthcare, proposing an innovative framework aimed at refining the organization and management of medical processes within healthcare institutions. Through the lens of detailed case studies, including stroke diagnosis leveraging the UNet model, and the identification of heart and respiratory diseases via machine learning algorithms applied to data from wearable devices, the study illuminates the profound capabilities of Big Data technologies in enhancing the precision of diagnostics, tailoring patient treatment, and elevating the overall efficiency of healthcare services. It meticulously interprets the outcomes of these applications, discusses the practical implications for healthcare professionals and institutions, confronts the challenges inherent in the integration of sophisticated analytics in clinical settings, and outlines potential directions for future research. Among the pivotal challenges highlighted are issues related to data privacy, security, the need for advanced infrastructure, and the imperative for ongoing training and interdisciplinary cooperation to navigate the complexities of Big Data in healthcare. The paper underscores the transformative promise of Big Data analytics, suggesting that comprehensive adoption and adept implementation could revolutionize healthcare delivery, making it more personalized, efficient, and cost-effective. Through this exploration, the paper contributes to the ongoing discourse on the integration of technology in healthcare, offering insights into how Big Data analytics can serve as a cornerstone for the next generation of medical diagnostics and patient care management, thereby enhancing health outcomes on a global scale.