Improving Prepayment Quality Control in Medicare Claim Processing

Abstract

Improper payments in Medicare claim processing present a recurring financial and operational challenge in Puerto Rico’s healthcare system. Frequent payment inaccuracies, caused by both human and system-level errors, have increased administrative costs and reduced providers’ confidence. To address this issue, a prepayment quality control framework was engineered using data integration, risk-based modeling, and automated workflow optimization. The methodology involved developing validated data pipelines, constructing predictive risk-scoring models, and implementing an automated claim routing process supported by SQL Server and CMS datasets. During the initial audit phase, financial discrepancies totaling $153,298.57 were identified; after the new framework’s implementation, financial error rates decreased from 5.12% on average to 0.11%. These results demonstrate that automation combined with targeted analyst calibration significantly enhances claim accuracy, operational efficiency, and compliance with CMS standards. The project validates an engineering approach capable of improving payment integrity and serving as a scalable model for healthcare financial systems.