Designing Automated Data Validation Pipelines for Policy-Driven Healthcare Eligibility Reporting in Cloud-Native Platforms
DOI:
https://doi.org/10.56830/IJSIE202608Keywords:
Healthcare Eligibility Reporting, Automated Data Validation, Policy-As-Code Governance, Cloud-Native Architecture, Healthcare Data QualityAbstract
Healthcare reporting depends on Electronic Health Record (EHR) systems, payer regulations, and regulatory entity eligibility requests. Claims policy non-conformance defects, fragmentation, inconsistencies, missed claims validation opportunities, and operational defects are examples of data-quality issues that may drive claim denials and claim compliance risk. The paper presents and assesses a novel automated pipeline approach to schema validation, semantic validation, Policy-as-Code (PaC) checks, operational validation, and data governance. The framework is implemented with Apache Spark, Kubernetes, Apache Airflow, and Open Policy Agent, and is applied to 2.5 Million Transactions across 15 Healthcare Organizations. Overall errors fell from 3.2% to 2.1%, a 34.4% reduction. Processing time improved by 82.5%, throughput increased by the same amount from 4,800 transactions per minute (TPM) to 11,600 TPM, policy violations fell by 48.9%, and audit readiness improved from 55% to 97%. This suggests that regulation-based validation can positively impact reporting accuracy, governance, operational efficiency, and compliance readiness. Future research will focus on systems with adaptive policy learning, FHIR deserialization at runtime, and AI-based anomaly detection in dynamic healthcare contexts.
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