End-to-End Data Pipeline Design for Medicaid Claims and Encounter Reporting
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I3P107Keywords:
Medicaid Claims Reporting, Encounter Data Management, Healthcare Data Pipelines, HL7 FHIR Integration, X12 Transaction Standards, CMS Compliance, Healthcare Interoperability, Cloud-Native Data Architecture, Real-Time Healthcare AnalyticsAbstract
Designing a reliable end-to-end data pipeline for Medicaid claims and encounter reporting has become increasingly critical as healthcare systems transition toward value-based care, interoperability, and real-time analytics. This study presents a scalable and standards-compliant pipeline architecture that supports the ingestion, transformation, validation, and reporting of Medicaid claims and encounter data across heterogeneous sources. The proposed framework integrates batch and streaming data processing using modern data engineering tools while ensuring compliance with regulatory requirements such as HIPAA and CMS reporting standards. The pipeline begins with multi-source data ingestion, incorporating Electronic Health Records (EHRs), managed care organization (MCO) submissions, and third-party billing systems. Data is processed through a layered architecture consisting of raw, curated, and analytics-ready zones, enabling efficient schema standardization and data harmonization using HL7 FHIR and X12 transaction formats. Advanced data validation mechanisms, including rule-based and machine learning-assisted anomaly detection, are implemented to improve data quality, reduce claim denials, and ensure accurate encounter reporting. A key contribution of this work is the integration of metadata-driven orchestration and automated quality checks, which significantly enhance pipeline transparency and traceability. The architecture also incorporates cloud-native technologies such as distributed storage, containerized workflows, and serverless processing to support scalability and cost optimization. Additionally, the pipeline supports near real-time reporting capabilities, allowing state Medicaid agencies and stakeholders to monitor utilization patterns, detect fraud, and improve decision-making. Evaluation of the proposed design demonstrates improvements in data latency, reporting accuracy, and operational efficiency compared to traditional legacy systems. The findings suggest that adopting a modern, interoperable data pipeline can significantly enhance Medicaid program oversight and healthcare delivery outcomes.
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