Predictive Analytics for Identifying High-Risk Medicare Patients: Enhancing Preventive Care

The intended purpose of this paper is to carry out an analysis of the use of predictive modeling for the early identification of high-risk Medicare patients with the aim of enhancing preventative measures. MEDI-CARE, a vital policy in the United States of America, which focuses on offering health care services to the old and people with disabilities, has been criticized over such factors as Shortcoming of Medicare hence can be attributed to factors such as the growing costs of health care and the emerging cases of chronic diseases. A new type of risk stratification: The use of predictive analytics, which incorporates ML-AI, is also in the early warning system and to monitor or follow up on the patient’s adverse health event, subsequent to which care coordination plans can be formulated. The abstract, hence, is an explanation of the roles of preventive and predictive analytics, especially in health care, with such a thing as the identification of early high-risk patients and management. They are that there is a rise in the dependence on methodologies that are data-driven for the purpose of attaining enhanced healthcare outcomes, reduction in the hospitalization quotient and the effective management of diseases which occur chronically. A few such points comprise data and information from electronic health records and other digital healthcare databases, model construction, and model application. It also addresses the various ethical pitfalls likely to arise when using the concept of predictive analytics, and these include the privacy of the patient’s data, how the collected data will be protected and filtered, as well as the prejudice that might be experienced when developing the algorithm. Current and earlier models of prediction, as well as their application in Medicare and research limitations, are described in the literature review of the article. Whereas in the methodology section, the procedures used in data collection, modeling, and validation are described, the results and discussion section put into perspective the effectiveness of the work done in enhancing Preventive care through predictive analytics. Finally, the discussion in the conclusion shall be based on the findings of the study and recommend future research directions for policy making.