Predictive Analytics for Medicare - Medicaid Pharmacy Billing and Claims

Fayazoddin Mohamad

Abstract: Medicare and Medicaid billing constitute a large proportion of pharmacy revenues in the United States, yet these processes are often prone to errors, fraud, and overpayments. This paper explores the design and implementation of predictive models aimed at improving compliance and financial outcomes within Medicare - Medicaid pharmacy billing. We present a framework that leverages data mining, advanced fraud detection algorithms, and real - time analytics dashboards to flag high - risk claims preemptively. Drawing on a dataset of over 2.5 million pharmacy transactions, our methodology integrates machine learning (ML) models such as random forests, gradient boosting, and anomaly detection techniques. The results reveal a reduction in billing discrepancies by up to 37% and improved claim reimbursement speed by 28% for participating pharmacies. We also discuss the regulatory implications, system architecture, and deployment considerations necessary for scaling this approach. Our findings suggest that predictive analytics can serve as a cornerstone for proactive compliance, minimizing both financial losses and regulatory risks, and streamlining reimbursement processes in an industry facing increasing complexity.

Keywords: Predictive Analytics, Medicare, Medicaid, Pharmacy Billing, Fraud Detection, Data Mining, Compliance, Machine Learning

How to Cite?: Fayazoddin Mohamad, "Predictive Analytics for Medicare - Medicaid Pharmacy Billing and Claims", Volume 12 Issue 12, December 2023, International Journal of Science and Research (IJSR), Pages: 2200-2204, https://www.ijsr.net/getabstract.php?paperid=SR23122232923, DOI: https://dx.doi.org/10.21275/SR23122232923