Real-time Clinical Decision Systems: Advancing Healthcare through ML-Driven Optimization

Mohit Agarwal

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 1977-1985

Abstract: This comprehensive article explores the transformative impact of real-time clinical decision systems and machine learning-driven optimization in modern healthcare settings. The article examines the integration of advanced technologies across various healthcare domains, from technical infrastructure to clinical applications, highlighting their role in improving patient care outcomes and operational efficiency. The article shows the implementation of sophisticated ML frameworks, addressing critical challenges in data privacy, security, and model interpretability while emphasizing the importance of clinical validation and regulatory compliance. Through article analysis of current implementations and emerging trends, the article demonstrates how these systems revolutionize healthcare delivery by enabling predictive diagnostics, precision medicine, and resource optimization. The article further explores the technical infrastructure requirements, clinical applications, and implementation challenges while providing insights into future opportunities and potential impacts on healthcare transformation. This article offers valuable insights for healthcare organizations seeking to implement or enhance their clinical decision support systems while maintaining high standards of patient care and operational excellence.

Keywords: Clinical Decision Support Systems; Healthcare Machine Learning; Real-time Healthcare Analytics; Medical Infrastructure Optimization; Healthcare Technology Implementation (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112199
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:863

DOI: 10.32628/CSEIT251112199

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