Modernizing Claims Adjudication Systems with NoSQL and Apache Hive in Medicaid Expansion Programs

Abstract

Medicaid's expansion under the Affordable Care Act (ACA) has greatly raised the volume and complexity of healthcare claims, thereby pressuring current systems of claims adjudication.    Often based on conventional relational database management systems (RDBMS), outdated systems struggle to meet processing high-throughput data needs while assuring real-time analytics and compliance. Many states now have performance limits, limited data integration capabilities, and scaling challenges—especially in recently expanded areas witnessing increased demand. The application of current data solutions—more notably, Apache Hive and NoSQL databases—to change Medicaid claims adjudication is investigated in this article. NoSQL provides quick processing of structured and unstructured claim data by means of a flexible, schema-less architecture appropriate for many data kinds. Apache Hive offers a SQL-like interface for querying large datasets maintained in Hadoop-based systems without technical programming skills, therefore allowing analysts and auditors to extract information. Together, these technologies solve fundamental problems of scalability, speed, and analytical profundity, therefore allowing more flexible decision-making and effective service delivery. The modernization initiative has improved policy effectiveness evaluation, speed of claims processing, delay reduction, beneficiary monitoring, and fraud detection. Turning now to a more scalable, data-driven adjudication system enables Medicaid programs to effectively serve expanding populations and satisfy changing needs of healthcare management in a digital age. This study underlines, coupled with significant technology approaches and implementation issues, the primary organizational effects of migrating to NoSQL and Hive-based architectures in the public healthcare sector.