Reducing Hospital Readmissions with IoT and Predictive Analytics: A Machine Learning Approach

Authors

  • Prof. shyam sunder

Abstract

Hospital readmissions pose significant challenges to healthcare systems, often indicating suboptimal patient care. This paper proposes a machine learning approach to reduce hospital readmissions by leveraging IoT devices and predictive analytics. We develop a predictive model that analyzes data from IoT-enabled patient monitoring systems to identify patients at high risk of readmission. The model's predictions are used to tailor post-discharge care plans, aiming to prevent readmissions. A retrospective analysis of hospital data shows that our approach can significantly reduce readmission rates, improving patient outcomes and reducing healthcare costs. The paper also discusses the implementation challenges and future research directions.

 

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Published

2024-07-03

How to Cite

sunder, P. shyam. (2024). Reducing Hospital Readmissions with IoT and Predictive Analytics: A Machine Learning Approach. International Journal of Sustainable Devlopment in Field of IT, 16(16). Retrieved from https://journals.threws.com/index.php/IT/article/view/257

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Section

Articles