Predictive Analytics in Medicare: Reducing Hospital Readmissions through AI-Driven Insights
Abstract: Readmissions are currently a big problem in the sphere of health care, especially when it comes to Medicare patients. These may include a lack of appropriate follow-up care, improper patient handling, inadequate control, and multiple issues surrounding chronic diseases, which are common among elderly patients. The CMS has singled out the challenge of decreasing hospital readmission rates as an important issue because of the possibility of enhancing clients' lives and reducing health costs. However, conventional approaches for developing and early detecting readmissions usually fail because of the peculiarities and heterogeneity of patients' data. The presence of predictive analytics based on AI and ML presents a revolutionary possibility to this issue. At the same time, predictive analytics utilizes big data and powerful tools of analysis to identify patterns and risks hidden from clinicians' eyes. This article discusses the potential of AI-driven predictive analytics to reduce hospital readmissions among Medicare patients. It addresses the limitations of conventional readmission reduction methods and explores how predictive analytics can help identify high-risk patients, allowing for timely interventions. The research focuses on developing and evaluating AI models, such as Gradient Boosting, to predict readmission risks and suggests personalized care plans to mitigate these risks.
Keywords: Predictive Analytics, Medicare, Hospital Readmissions, Machine Learning, AI in Healthcare, Patient
How to Cite?: Ginoop Chennekkattu Markose, "Predictive Analytics in Medicare: Reducing Hospital Readmissions through AI-Driven Insights", Volume 13 Issue 9, September 2024, International Journal of Science and Research (IJSR), Pages: 850-857, https://www.ijsr.net/getabstract.php?paperid=SR24908002419, DOI: https://dx.doi.org/10.21275/SR24908002419